System and method of a biosensor for detection of microvascular responses

ABSTRACT

An optical circuit detects optical signals reflected from skin tissue at one or more different wavelengths. A processing circuit integrated with the optical circuit or in communication with the optical circuit identifies an insulin release event using at least one optical signal at a first wavelength. A frequency of insulin release events is determined and in response to the frequency of the insulin release events, vascular imaging or a vascular test is delayed or performed.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 120 as acontinuation to U.S. patent application Ser. No. 16/433,947 entitledSYSTEM AND METHOD OF A BIOSENSOR FOR DETECTION OF MICROVASCULARRESPONSES, filed Jun. 6, 2019, and hereby expressly incorporated byreference herein, which claims priority under 35 U.S.C. § 120 as acontinuation in part to U.S. patent application Ser. No. 16/172,661entitled, “SYSTEM AND METHOD OF A BIOSENSOR FOR DETECTION OFVASODILATION,” filed Oct. 26, 2018, and hereby expressly incorporated byreference herein, which claims priority under 35 U.S.C. § 119(e) to:

U.S. Provisional Application No. 62/675,151 entitled, “SYSTEM AND METHODOF A BIOSENSOR FOR DETECTION OF VASODILATION,” filed May 22, 2018, andhereby expressly incorporated by reference herein;

U.S. Provisional Application No. 62/577,707 entitled, “SYSTEM AND METHODFOR HEALTH MONITORING OF AN ANIMAL USING A MULTI-BAND BIOSENSOR,” filedOct. 26, 2017, and hereby expressly incorporated by reference herein;and

U.S. Provisional Application No. 62/613,388 entitled, “SYSTEM AND METHODFOR INFECTION DISCRIMINATION USING PPG TECHNOLOGY,” filed Jan. 3, 2018,and hereby expressly incorporated by reference herein.

The present application claims priority under 35 U.S.C. § 120 as acontinuation in part to U.S. patent application Ser. No. 16/270,268entitled, “SYSTEM AND METHOD FOR GLUCOSE MONITORING,” filed Feb. 7,2019, and hereby expressly incorporated by reference herein which claimspriority under 35 U.S.C. § 120 as a continuation application to U.S.patent application Ser. No. 15/811,479 entitled, “SYSTEM AND METHOD FORA BIOSENSOR INTEGRATED IN A VEHICLE,” filed Nov. 13, 2017, now U.S. Pat.No. 10,238,346 issued Mar. 26, 2019 and hereby expressly incorporated byreference herein, which claims priority under 35 U.S.C. § 120 as acontinuation in part application to U.S. patent application Ser. No.15/490,813 entitled, “SYSTEM AND METHOD FOR HEALTH MONITORING USING ANON-INVASIVE, MULTI-BAND BIOSENSOR,” filed Apr. 18, 2017, now U.S. Pat.No. 9,980,676 issued May 29, 2018 which claims priority under 35 U.S.C.§ 120 as a continuation application to U.S. patent application Ser. No.15/275,388 entitled, “SYSTEM AND METHOD FOR HEALTH MONITORING USING ANON-INVASIVE, MULTI-BAND BIOSENSOR,” filed Sep. 24, 2016, now U.S. Pat.No. 9,642,578 issued May 9, 2017.

The present application claims priority under 35 U.S.C. § 120 as acontinuation in part application to U.S. patent application Ser. No.16/183,354 entitled, “SYSTEM AND METHOD FOR HEALTH MONITORING BY AN EARPIECE,” filed Nov. 7, 2018 and hereby expressly incorporated byreference herein, which claims priority under 35 U.S.C. § 120 as acontinuation application to U.S. patent application Ser. No. 15/485,816entitled, “SYSTEM AND METHOD FOR A DRUG DELIVERY AND BIOSENSOR PATCH,”filed Apr. 12, 2017, now U.S. Pat. No. 10,155,087 issued Dec. 18, 2018and hereby expressly incorporated by reference herein, which claimspriority under 35 U.S.C. § 120 as a continuation application to U.S.Utility application Ser. No. 15/276,760, entitled, “SYSTEM AND METHODFOR A DRUG DELIVERY AND BIOSENSOR PATCH,” filed Sep. 26, 2016, now U.S.Pat. No. 9,636,457 issued May 2, 2017, which is hereby expresslyincorporated by reference herein.

The present application claims priority under 35 U.S.C. § 120 as acontinuation in part application to U.S. patent application Ser. No.15/718,721 entitled, “SYSTEM AND METHOD FOR MONITORING NITRIC OXIDELEVELS USING A NON-INVASIVE, MULTI-BAND BIOSENSOR,” filed Sep. 28, 2017,now U.S. Pat. No. 10,517,515 issued Dec. 31, 2019, and hereby expresslyincorporated by reference herein, which claims priority as acontinuation application to U.S. Utility application Ser. No. 15/622,941entitled, “SYSTEM AND METHOD FOR MONITORING NITRIC OXIDE LEVELS USING ANON-INVASIVE, MULTI-BAND BIOSENSOR,” filed Jun. 14, 2017, now U.S. Pat.No. 9,788,767 issued Oct. 17, 2017, and hereby expressly incorporated byreference herein, which claims priority under 35 U.S.C. § 119 to U.S.Provisional Application No. 62/463,104 entitled, “SYSTEM AND METHOD FORMONITORING NITRIC OXIDE LEVELS USING A NON-INVASIVE, MULTI-BANDBIOSENSOR,” filed Feb. 24, 2017, and hereby expressly incorporated byreference herein.

The present application claims priority under 35 U.S.C. § 120 as acontinuation in part application to U.S. patent application Ser. No.15/404,117 entitled, “SYSTEM AND METHOD FOR HEALTH MONITORING INCLUDINGA USER DEVICE AND BIOSENSOR,” filed Jan. 11, 2017 and hereby expresslyincorporated by reference herein.

The present application claims priority under 35 U.S.C. § 120 as acontinuation in part application to U.S. Utility application Ser. No.15/958,620 entitled, “SYSTEM AND METHOD FOR DETECTING A HEALTH CONDITIONUSING AN OPTICAL SENSOR,” filed Apr. 20, 2018, now U.S. Pat. No.10,524,720 issued Jan. 7, 2020, and hereby expressly incorporated byreference herein which claims priority under 35 U.S.C. § 120 as acontinuation application to U.S. Utility application Ser. No. 15/680,991entitled, “SYSTEM AND METHOD FOR DETECTING A SEPSIS CONDITION,” filedAug. 18, 2017, now U.S. Pat. No. 9,968,289 issued May 15, 2018 andhereby expressly incorporated by reference herein.

The present application claims priority under 35 U.S.C. § 120 as acontinuation in part application to U.S. patent application Ser. No.15/400,916 entitled, “SYSTEM AND METHOD FOR HEALTH MONITORING INCLUDINGA REMOTE DEVICE,” filed Jan. 6, 2017 and hereby expressly incorporatedby reference herein.

The present application claims priority under 35 U.S.C. § 120 as acontinuation in part to U.S. patent application Ser. No. 16/019,518entitled, “SYSTEM AND METHOD FOR BLOOD TYPING USING PPG TECHNOLOGY,”filed Jun. 26, 2018, and hereby expressly incorporated by referenceherein, which claims priority under 35 U.S.C. § 120 as a divisional toU.S. patent application Ser. No. 15/867,632 entitled, “SYSTEM AND METHODFOR BLOOD TYPING USING PPG TECHNOLOGY,” filed Jan. 10, 2018, now U.S.Pat. No. 10,039,500 issued Aug. 7, 2018 and hereby expresslyincorporated by reference herein.

The present application claims priority under 35 U.S.C. § 120 as acontinuation in part to U.S. patent application Ser. No. 16/208,358entitled, “VEHICLULAR HEALTH MONITORING SYSTEM AND METHOD,” filed Dec.3, 2018 which claims priority as a continuation to U.S. patentapplication Ser. No. 15/859,147 entitled, “VEHICLULAR HEALTH MONITORINGSYSTEM AND METHOD,” filed Dec. 29, 2017, now U.S. Pat. No. 10,194,871issued Feb. 5, 2019 and both of which are hereby expressly incorporatedby reference herein.

The present application claims priority under 35 U.S.C. § 120 as acontinuation in part to U.S. patent application Ser. No. 15/898,580entitled, “SYSTEM AND METHOD FOR OBTAINING HEALTH DATA USING A NEURALNETWORK,” filed Feb. 17, 2018, and hereby expressly incorporated byreference herein.

FIELD

This application relates to systems and methods of non-invasive healthmonitoring, and in particular, a system and method for detection ofvascular health using an optical sensor.

BACKGROUND

A person's vitals, such as temperature, blood oxygen levels, respirationrate, relative blood pressure, etc., may need to be monitoredperiodically typically using one or more instruments. For example,instruments for obtaining vitals of a user include blood pressure cuffs,thermometers, SpO₂ measurement devices, glucose level meters, etc. Thedetection of substances and measurement of concentration level orindicators of various substances in a user's blood stream is importantin health monitoring. Currently, detection of concentration levels ofblood substances is performed by drawing blood from a blood vessel usinga needle and syringe. The blood sample is then transported to a lab foranalysis. This type of monitoring is invasive, non-continuous and timeconsuming.

One current non-invasive method is known for measuring the oxygensaturation of blood using pulse oximeters. Pulse oximeters detect oxygensaturation of hemoglobin by using, e.g., spectrophotometry to determinespectral absorbencies and determining concentration levels of oxygenbased on Beer-Lambert law principles. In addition, pulse oximetry mayuse photoplethysmography (PPG) methods for the assessment of oxygensaturation in pulsatile arterial blood flow. The subject's skin at a‘measurement location’ is illuminated with two distinct wavelengths oflight and the relative absorbance at each of the wavelengths isdetermined. For example, a wavelength in the visible red spectrum (forexample, at 660 nm) has an extinction coefficient of hemoglobin thatexceeds the extinction coefficient of oxihemoglobin. At a wavelength inthe near infrared spectrum (for example, at 940 nm), the extinctioncoefficient of oxihemoglobin exceeds the extinction coefficient ofhemoglobin. The pulse oximeter filters the absorbance of the pulsatilefraction of the blood, i.e. that due to arterial blood (AC components),from the constant absorbance by nonpulsatile venous or capillary bloodor tissue pigments (DC components), to eliminate the effect of tissueabsorbance to measure the oxygen saturation of arterial blood.

For example, when the heart pumps blood to the body and the lungs duringsystole, the amount of blood that reaches the capillaries in the skinsurface increases, resulting in more light absorption. The blood thentravels back to the heart through the venous network, leading to adecrease of blood volume in the capillaries and less light absorption.The measured PPG waveform therefore comprises a pulsatile (often called“AC”) physiological waveform that reflects cardiac synchronous changesin the blood volume with each heartbeat, which is superimposed on a muchlarger slowly varying quasi-static (“DC”) baseline. The use of PPGtechniques as heretofore been used for measurement of the oxygensaturation of blood in vessels.

As such, there is a need for a non-invasive health monitoring system andmethod that monitors health conditions of a user non-invasively,continuously and in real time.

In particular, there is a need for an improved system and method fordetection of vascular health and conditions affected by vascular health.

SUMMARY

According to a first aspect, A device includes an optical circuitconfigured to detect photoplethysmography (PPG) signals, wherein a firstPPG signal includes a first spectral response around a first wavelengthobtained from light reflected from or transmitted through tissue of auser and a second PPG signal includes a second spectral response arounda second wavelength obtained from light reflected from or transmittedthrough the tissue of the user. The device further includes one or moreprocessing circuits configured to identify an insulin release eventusing the first PPG signal and the second PPG signal, wherein theinsulin release event is a pulse of insulin in blood flow of the userand determine a frequency of insulin release events.

According to a second aspect, a system includes an optical circuitconfigured to obtain at least a first PPG signal including a firstspectral response around a first wavelength obtained from lightreflected from or transmitted through tissue of a user. The systemfurther includes at least one processing circuit configured to detect aninsulin release event using the first PPG signal, wherein the insulinrelease event is a pulse of insulin in blood flow of the user anddetermine to delay vascular imaging or tests based on detection of theinsulin release event.

According to a third aspect, a biosensor includes an optical circuitconfigured to obtain a first PPG signal including a first spectralresponse around a first wavelength obtained from light reflected from ortransmitted through tissue of a user. The biosensor further includes atleast one processing circuit configured to determine a frequency ofinsulin release events using the first PPG signal, wherein the insulinrelease events are a pulse of insulin in blood flow of the user.

In one or more of the above aspects, the one or more processing circuitsare further configured to determine to perform tests or vascular imagingin response to the frequency of insulin release events or to determineto delay vascular imaging or tests in response to the frequency ofinsulin release events.

In one or more of the above aspects, the one or more processing circuitsare further configured to identify the insulin release event bydetermining an R value curve using a ratio value obtained from a firstAC component of the first PPG signal and a second AC component of thesecond PPG signal and identifying the insulin release event using the Rvalue curve.

In one or more of the above aspects, the one or more processing circuitsare further configured to identify the insulin release event bycomparing the R value curve to one or more R value curve patternsindicative of an insulin release event.

In one or more of the above aspects, wherein the one or more processingcircuits are further configured to determine an insulin level during theinsulin release event by determining an R value curve during the insulinrelease event using a ratio value obtained from a first AC component ofthe first PPG signal and a second AC component of the second PPG signal;determining an integral area of the R value curve during the insulinrelease event; and determining the insulin level using the area of the Rvalue curve and a calibration.

In one or more of the above aspects, the one or more processing circuitsare further configured to identify a number of insulin release eventsduring a predetermined time period and determine at least one of: astage of digestion, an estimated time since caloric intake or a level ofhunger.

In one or more of the above aspects, the one or more processing circuitsare further configured to determine a correlation signal during theinsulin release event between the first PPG signal and the second PPGsignal, wherein the correlation signal includes a phase delay betweenthe first PPG signal and the second PPG signal or a pulse shapecorrelation between the first PPG signal and the second PPG signal.

In one or more of the above aspects, the one or more processing circuitsare further configured to determine a level of vasoconstriction orvasodilation using the correlation signal during the insulin releaseevent.

In one or more of the above aspects, the one or more processing circuitsare further configured to compare the level of vasoconstriction orvasodilation to a predetermined range measured from a general populationwith healthy vascular systems and determine a balance of efficacy ofendothelin (ET-1) and nitric oxide (NO) during the insulin releaseevent.

In one or more of the above aspects, the one or more processing circuitsare further configured to determine a measurement of vascular healthusing the correlation signal.

In one or more of the above aspects, the one or more processing circuitsare further configured to determine a vascular dysfunction in the user;determine a ratio value obtained from a first AC component of the firstPPG signal and a second AC component of the second PPG signal; access anindividual calibration table between predetermined ratio values andglucose levels; and obtain a glucose level using the individualcalibration and the ratio value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic block diagram of exemplary components inan embodiment of the biosensor.

FIG. 2 illustrates a schematic block diagram of an embodiment of the PPGcircuit in more detail.

FIG. 3 illustrates a logical flow diagram of an embodiment of a methodfor determining concentration level of a substance in blood flow usingBeer-Lambert principles.

FIG. 4 illustrates the spectral response obtained at the plurality ofwavelengths with the systolic points and diastolic points aligned over acardiac cycle.

FIG. 5 illustrates a logical flow diagram of an embodiment of a methodof the biosensor.

FIG. 6 illustrates a logical flow diagram of an exemplary method todetermine levels of a substance in blood flow using the PPG signals at aplurality of wavelengths.

FIG. 7 illustrates a logical flow diagram of an exemplary method todetermine levels of a substance using the spectral responses at aplurality of wavelengths in more detail.

FIG. 8 illustrates a logical flow diagram of an exemplary embodiment ofa method for measuring a concentration level of a substance in vivousing shifts in absorbance spectra.

FIG. 9 illustrates a schematic drawing of an exemplary embodiment of aspectral response obtained using an embodiment of the biosensor.

FIG. 10 illustrates a schematic drawing of an exemplary embodiment ofresults of R values determined using a plurality of methods.

FIG. 11A illustrates a schematic drawing of an exemplary embodiment ofan empirical calibration curve for correlating oxygen saturation levels(SpO₂) with R values.

FIG. 11B illustrates a schematic drawing of an exemplary embodiment ofan empirical calibration curve for correlating NO levels with R values.

FIG. 12 illustrates a schematic block diagram of an embodiment of acalibration database.

FIG. 13 illustrates a logical flow diagram of an embodiment of a methodfor using a machine learning neural network technique for detection ofhealth data.

FIG. 14 illustrates a schematic diagram of a graph of PPG signals duringa period of vasodilation in vessels.

FIG. 15 illustrates a schematic diagram of a series of graphsillustrating the effects of vasodilation in PPG signals.

FIG. 16 illustrates a schematic diagram illustrating phase differencesand average low frequency levels during vasodilation of PPG signals ofvarious wavelengths.

FIG. 17A illustrates a schematic block diagram of an arterial wall underhealthy conditions.

FIG. 17B illustrates a schematic block diagram of an arterial wall withvascular dysfunction.

FIG. 18 illustrates a schematic diagram of PPG signals obtained duringperiods of insulin release in vessels.

FIG. 19 illustrates a schematic diagram of graphs comparing phase offsetand pulse shape waveform in a plurality of PPG signals during insulinrelease in vivo.

FIG. 20 illustrates a schematic block diagram of an insulin response ofa young healthy male and a middle-aged male.

FIG. 21 illustrates a schematic diagram of graphs comparing phase offsetand pulse shape waveform in a plurality of PPG signals during insulinrelease in an adolescent male.

FIG. 22 illustrates a schematic diagram of an insulin response in theadolescent male in greater detail.

FIG. 23 illustrates a schematic diagram of an insulin response in theadolescent male in greater detail.

FIG. 24 illustrates a schematic diagram of graphs comparing phase offsetand pulse shape waveform in a plurality of PPG signals during insulinrelease in a middle-aged male.

FIG. 25 illustrates a schematic diagram of an insulin response in amiddle-aged male in greater detail.

FIG. 26 illustrates a schematic diagram of an insulin response in amiddle-aged male in greater detail.

FIG. 27 illustrates a schematic flow diagram of an embodiment of amethod for determining vascular health using the biosensor.

FIG. 28 illustrates a schematic flow diagram of an embodiment of amethod for determining an efficacy balance of ET-1 and NO in smoothmuscle cells of vessels.

FIG. 29 illustrates a schematic flow diagram of an embodiment of amethod 2900 for determining an insulin level in blood flow.

FIG. 30 illustrates schematic diagrams of measurements of glucose levelsin a plurality of patients using the biosensor in a clinical trial.

FIG. 31 illustrates schematic diagrams of measurements of glucose levelsin a plurality of patients using the biosensor in a clinical trial.

FIG. 32 illustrates a schematic flow diagram of an embodiment of amethod for determining glucose levels of a patient with atypicalvascular function.

FIG. 33 illustrates a schematic flow diagram of another embodiment of amethod for determining glucose levels of a patient with atypicalvascular function.

FIG. 34 illustrates a schematic diagram of graphs of PPG signals duringdeep inhalation.

FIG. 35 illustrates a schematic diagram of graphs of PPG signalsdetected from a critical care patient diagnosed with sepsis.

FIG. 36 illustrates a schematic diagram of graphs of PPG signals duringperiods of ingestion and fasting.

FIG. 37 illustrates a schematic flow diagram of an embodiment of amethod for identifying a PPG feature, such as an insulin release pulseor deep inhalation pulse.

FIG. 38 illustrates an elevational view of a biosensor configured forattachment to an appendage.

FIG. 39 illustrates an elevational view of a biosensor configured in aring.

DETAILED DESCRIPTION

The word “exemplary” or “embodiment” is used herein to mean “serving asan example, instance, or illustration.” Any implementation or aspectdescribed herein as “exemplary” or as an “embodiment” is not necessarilyto be construed as preferred or advantageous over other aspects of thedisclosure. Likewise, the term “aspects” does not require that allaspects of the disclosure include the discussed feature, advantage, ormode of operation.

Embodiments will now be described in detail with reference to theaccompanying drawings. In the following description, numerous specificdetails are set forth in order to provide a thorough understanding ofthe aspects described herein. It will be apparent, however, to oneskilled in the art, that these and other aspects may be practicedwithout some or all of these specific details. In addition, well knownsteps in a method of a process may be omitted from flow diagramspresented herein in order not to obscure the aspects of the disclosure.Similarly, well known components in a device may be omitted from figuresand descriptions thereof presented herein in order not to obscure theaspects of the disclosure.

Overview

The release of the Endothelium-derived relaxing factor (EDRF) causes thearteries to expand in diameter and change elasticity, commonly referredto as vasodilation. Flow-mediated vasodilation measurements have beenperformed in human studies and are of diagnostic and prognosticimportance. Prior techniques for measuring vasodilation require usinghigh-frequency ultrasound to visually inspect vessels, most commonly thebrachial artery. For example, one ultrasound technique evaluatesflow-mediated vasodilation (FMD), an endothelium-dependent function, inthe brachial artery. This process includes applying a stimulus toprovoke the endothelium to release nitric oxide (NO) with subsequentvasodilation that is then imaged using high resolution ultrasonographyand quantitated as an index of vasomotor function. This process ofhigh-resolution ultrasonography of the brachial artery to evaluatevasomotor function has limitations. It must be performed in a clinicalsetting by a medical clinician using expensive ultrasonographyequipment.

Thus, there is a need for an improved system and method for detection ofvasodilation or vasoconstriction, and vascular health.

Embodiment of the Biosensor

In an embodiment, a biosensor includes an optical sensor orphotoplethysmography (PPG) circuit configured to transmit light at aplurality of wavelengths directed at skin tissue of a user or patient.The user/patient may include any animal, human or non-human. The PPGcircuit detects the light reflected from the skin tissue or transmittedthrough the skin tissue and generates one or more spectral responses atone or more wavelengths. A processing circuit integrated in thebiosensor or in communication with the biosensor processes the spectraldata to obtain a user's vitals, concentrations of substances in bloodflow and/or other health information.

FIG. 1 illustrates a schematic block diagram of exemplary components inan embodiment of the biosensor 100. The biosensor 100 is configured todetect oxygen saturation (SaO2 or SpO2) levels in blood flow, as well asconcentration levels of one or more other substances in blood flow of auser. In addition, the biosensor 100 is configured to detect a level ofvasodilation and/or a period of vasodilation using one or moremeasurement techniques as described in more detail herein. The biosensor100 includes a PPG circuit 110 as described in more detail herein.

The biosensor 100 may include one or more processing circuits 102communicatively coupled to a memory device 104. In one aspect, thememory device 104 may include one or more non-transitory processorreadable memories that store instructions which when executed by the oneor more processing circuits 102, causes the one or more processingcircuits 102 to perform one or more functions described herein. Theprocessing circuit 102 may be co-located with one or more of the othercircuits of the biosensor 100 in a same physical circuit board orlocated separately in a different circuit board or encasement. Theprocessing circuit 102 may also be communicatively coupled to a centralcontrol module or server in a remote location as described furtherherein. The biosensor 100 may be battery operated and include a battery118, such as a lithium ion battery. The memory device 104 may storespectral data 106 or health data 120 obtained by the biosensor 100.

The biosensor 100 may include a temperature sensor 114 configured todetect a temperature of a user. For example, the temperature sensor 108may include an array of sensors (e.g., 16×16 pixels) to detect a skintemperature of a user. The temperature sensor 114 may also be used tocalibrate the PPG circuit 110, such as the wavelength output of LEDs orother light sources. The biosensor 100 may include a display 116 todisplay biosensor data or control interfaces for the biosensor 100.

The biosensor 100 further includes a transceiver 112. The transceiver112 may include a wireless or wired transceiver configured tocommunicate with or with one or more devices over a LAN, MAN and/or WAN.In one aspect, the wireless transceiver may include a Bluetooth enabled(BLE) transceiver or IEEE 802.11ah, Zigbee, IEEE 802.15-11 or WLAN (suchas an IEEE 802.11 standard protocol) compliant transceiver. In anotheraspect, the wireless transceiver may operate using RFID, short rangeradio frequency, infrared link, or other short range wirelesscommunication protocol. In another aspect, the wireless transceiver mayalso include or alternatively include an interface for communicatingover a cellular network. The transceiver 112 may also include a wiredtransceiver interface, e.g., a USB port or other type of wiredconnection, for communication with one or more other devices over a LAN,MAN and/or WAN. The transceiver 112 may include a wireless or wiredtransceiver configured to communicate with a vehicle or its componentsover a controller area network (CAN), Local Interconnect Network (LIN),Flex Ray, Media Oriented Systems Transport (MOST), (On-Board DiagnosticsII), Ethernet or using another type of network or protocol. Thebiosensor 100 may transmit health data using the transceiver 112 over awide area network, such as a cellular network, to a third party serviceprovider, such as a health care provider or emergency service provider.

Embodiment—PPG Circuit

FIG. 2 illustrates a schematic block diagram of an embodiment of the PPGcircuit 110 in more detail. The PPG circuit 110 includes a light source210 configured to emit a plurality of wavelengths of light acrossvarious spectrums. The plurality of LEDs 212 a-n are configured to emitlight in one or more spectrums, including infrared (IR) light,ultraviolet (UV) light, near IR light or visible light, in response todriver circuit 218. For example, the biosensor 100 may include a firstLED 212 a that emits visible light and a second LED 212 b that emitsinfrared light and a third LED 212 c that emits UV light, etc. Inanother embodiment, one or more of the light sources 210 may includetunable LEDs or lasers operable to emit light over one or morefrequencies or ranges of frequencies or spectrums in response to drivercircuit 218.

In an embodiment, the driver circuit 218 is configured to control theone or more LEDs 212 a-n to generate light at one or more frequenciesfor predetermined periods of time. The driver circuit 218 may controlthe LEDs 212 a-n to operate concurrently or consecutively. The drivercircuit 218 is configured to control a power level, emission period andfrequency of emission of the LEDs 212 a-n. The driver circuit 218 mayalso tune a wavelength output of the LEDs212 a-n in response to atemperature or other feedback. The biosensor 100 is thus configured toemit one or more wavelengths of light in one or more spectrums that isdirected at the surface or epidermal layer of the skin tissue of a user.The emitted light 216 passes through at least one aperture 214 andtowards the surface or epidermal layer of the skin tissue of a user.

The PPG circuit 110 further includes one or more photodetector circuits230 a-n. The photodetector circuits 230 may be implemented as part of acamera 250. For example, a first photodetector circuit 230 may beconfigured to detect visible light and the second photodetector circuit230 may be configured to detect IR light. Alternatively, a singlephotodetector 230 may be implemented to detect light across multiplespectrums. When multiple photodetectors 230 are implemented, thedetected signals obtained from each of the photodetectors may be addedor averaged. Alternatively, a detected light signal with more optimalsignal to noise ration may be selected from the multiple photodetectorcircuits 230 a-n.

The first photodetector circuit 230 a and the second photodetectorcircuit 230 n may also include a first filter 260 and a second filter262 configured to filter ambient light and/or scattered light. Forexample, in some embodiments, only light reflected at an approximatelyperpendicular angle to the skin surface of the user is desired to passthrough the filters. The first photodetector circuit 230 a and thesecond photodetector circuit 230 n are coupled to a first analog todigital (A/D) circuit 236 and a second A/D circuit 238. Alternatively, asingle A/D circuit may be coupled to each of the photodetector circuits230 a-n. The A/D circuits convert the spectral responses to digitalspectral data for processing by a DSP or other processing circuit.

The one or more photodetector circuits 230 a-n include one or more typesof spectrometers or photodiodes or other types of light detectioncircuits configured to detect an intensity of light as a function ofwavelength over a time period to obtain a spectral response. In use, theone or more photodetector circuits 230 a-n detect the intensity ofreflected light 240 from skin tissue of a user that enters one or moreapertures 220 a-n of the biosensor 100. In another example, the one ormore photodetector circuits 230 a-n detect the intensity of light due totransmissive absorption (e.g., light transmitted through tissues, suchas a fingertip or ear lobe). The one or more photodetector circuits 230a-n then obtain a spectral response (a PPG signal) of the reflected ortransmissive light by measuring an intensity of the light at one or morewavelengths over a period of time.

In another embodiment, the light source 210 may include a broad spectrumlight source, such as a white light to infrared (IR) or near IR LED,that emits light with wavelengths across multiple spectrums, e.g. from350 nm to 2500 nm. Broad spectrum light sources with different rangesmay be implemented. In an aspect, a broad spectrum light source isimplemented with a range across 100 nm wavelengths to 2000 nm range ofwavelengths in the visible, IR and/or UV frequencies. For example, abroadband tungsten light source for spectroscopy may be used. Thespectral response of the reflected light 240 is then measured across thewavelengths in the broad spectrum, e.g. from 350 nm to 2500 nm,concurrently. In an aspect, a charge coupled device (CCD) spectrometermay be configured in the photodetector circuit 230 to measure thespectral response of the detected light over the broad spectrum.

The PPG circuit 110 may also include a digital signal processing (DSP)circuit 270 that includes signal processing of the digital spectraldata. For example, the DSP circuit may determine AC or DC componentsfrom the spectral responses (PPG signals) or diastolic and systolicpoints or other spectral data 106. The spectral data may then beprocessed by the processing circuit 102 to obtain health data 120 of auser. The spectral data 106 may alternatively or in additionally betransmitted by the biosensor 100 to a central control module forprocessing to obtain health data 120 of a user. The spectral data 106,PPG signals, etc. may be stored in the memory device 104 of thebiosensor 100.

In use, the biosensor 100 performs PPG techniques using the PPG circuit110 to detect the concentration levels of one or more substances inblood flow. In one aspect, the biosensor 100 receives reflected light ortransmissive light from skin tissue to obtain a spectral response. Thespectral response includes a spectral curve that illustrates anintensity or power or energy at a frequency or wavelength in a spectralregion of the detected light over a period of time. The ratio of theresonance absorption peaks from two different frequencies can becalculated and based on the Beer-Lambert law used to obtain the levelsof substances in the blood flow.

For example, one or more of the embodiments of the biosensor 100described herein is configured to detect a concentration level of one ormore substances within blood flow using PPG techniques. For example, thebiosensor 100 may detect nitric oxide (NO) concentration levels andcorrelate the NO concentration level to a blood glucose level. Thebiosensor 100 may also detect oxygen saturation (SaO2 or SpO2) levels inblood flow. The biosensor may also be configured to detect a liverenzyme cytochrome oxidase (P450) enzyme and correlate the P450concentration level to a blood alcohol level.

The spectral response of a substance or substances in the arterial bloodflow is determined in a controlled environment, so that an absorptioncoefficient α_(g1) can be obtained at a first light wavelength λ1 and ata second wavelength λ2. According to the Beer-Lambert law, lightintensity will decrease logarithmically with path length l (such asthrough an artery of length l). Assuming then an initial intensityI_(in) of light is passed through a path length l, a concentration C_(g)of a substance may be determined. For example, the concentration Cg maybe obtained from the following equations:

At the first wavelength λ₁ , I ₁ =I _(in1*)10^(−(α) ^(g1) ^(C) ^(gw)^(+α) ^(w1) ^(C) ^(w) ⁾*^(l)

At the second wavelength λ₁ , I ₂ =I _(in2*)10^(−(α) ^(g2) ^(C) ^(gw)^(+α) ^(w2) ^(C) ^(w) ⁾*^(l)

wherein:

I_(in1) is the intensity of the initial light at λ₁

I_(in2) is the intensity of the initial light at λ₂

α_(g1) is the absorption coefficient of the substance in arterial bloodat λ₁

α_(g2) is the absorption coefficient of the substance in arterial bloodat λ₂

α_(w1) is the absorption coefficient of arterial blood at λ₁

α_(w2) is the absorption coefficient of arterial blood at λ₂

C_(gw) is the concentration of the substance and arterial blood

C_(w) is the concentration of arterial blood

Then letting R equal:

$R = \frac{\log \; 10\left( \frac{I_{1}}{{Iin}\; 1} \right)}{\log \; 10\left( \frac{I2}{{Iin}\; 2} \right)}$

The concentration of the substance Cg may then be equal to:

${Cg} = {\frac{Cgw}{{Cgw} + {Cw}} = \frac{{\alpha_{w2}R} - \alpha_{w1}}{{\left( {\alpha_{W2} - \alpha_{gw2}} \right)*R} - \left( {\alpha_{w1} - \alpha_{gW1}} \right)}}$

The biosensor 100 may thus determine the concentration of varioussubstances in arterial blood flow from the Beer-Lambert principles usingthe spectral responses of at least two different wavelengths.

FIG. 3 illustrates a logical flow diagram of an embodiment of a method300 for determining concentration level of a substance in blood flowusing Beer-Lambert principles. The biosensor 100 transmits light at afirst predetermined wavelength and at a second predetermined wavelength.The biosensor 100 detects the light (reflected from the skin ortransmitted through the skin) and determines the spectral response atthe first wavelength at 302 and at the second wavelength at 304. Thebiosensor 100 then determines health data, such as an indicator orconcentration level of substances in blood flow, using the spectralresponses of the first and second wavelength at 306. In general, thefirst predetermined wavelength is selected that has a high absorptioncoefficient for the substance in blood flow while the secondpredetermined wavelength is selected that has a lower absorptioncoefficient for the substance in blood flow. Thus, it is generallydesired that the spectral response for the first predeterminedwavelength have a higher intensity level in response to the substancethan the spectral response for the second predetermined wavelength.

In an embodiment, the biosensor 100 may detect a concentration level ofnitric oxide (NO) in blood flow using a first predetermined wavelengthin a range of 380-410 nm and in particular at 390 nm or 395 nm. Inanother aspect, the biosensor 100 may transmit light at the firstpredetermined wavelength in a range of approximately 1 nm to 50 nmaround the first predetermined wavelength. Similarly, the biosensor 100may transmit light at the second predetermined wavelength in a range ofapproximately 1 nm to 50 nm around the second predetermined wavelength.The range of wavelengths is determined based on the spectral responsesince a spectral response may extend over a range of frequencies, not asingle frequency (i.e., it has a nonzero linewidth). The light that isreflected or transmitted by NO may spread over a range of wavelengthsrather than just the single predetermined wavelength. In addition, thecenter of the spectral response may be shifted from its nominal centralwavelength or the predetermined wavelength. The range of 1 nm to 50 nmis based on the bandwidth of the spectral response line and shouldinclude wavelengths with increased light intensity detected for thetargeted substance around the predetermined wavelength.

The first spectral response of the light over the first range ofwavelengths including the first predetermined wavelength and the secondspectral response of the light over the second range of wavelengthsincluding the second predetermined wavelengths is then generated at 302and 304. The biosensor 100 analyzes the first and second spectralresponses to detect an indicator or concentration level of NO in thearterial blood flow at 306. In another embodiment, using absorptioncoefficients for both Nitric Oxide and Hemoglobin, the concentration ofNitric Oxide can be obtained in arterial blood. A calibration tableusing human subjects may then correlate amounts of glucose (mG/DL) inrelation to R values (NoHb) 404/940 nm.

In another example, the biosensor 100 may also detect vitals, such asheart rate, respiration rate and pulse pressure. The biosensor 100 mayalso determine a level of vasodilation and a period of vasodilation asdescribed in more detail herein. Because blood flow to the skin can bemodulated by multiple other physiological systems, the biosensor 100 mayalso be used to monitor arterial health, such as hypovolemia or othercirculatory conditions.

Photoplethysmography (PPG) is used to measure time-dependent volumetricproperties of blood in blood vessels due to the cardiac cycle. Forexample, the heartbeat affects the volume of blood flow and theconcentration or absorption levels of substances being measured in thearterial blood flow. Over a cardiac cycle, pulsating arterial bloodchanges the volume of blood flow in a blood vessel. Incident light I_(O)is directed at a tissue site and a certain amount of light is reflectedor transmitted and a certain amount of light is absorbed. At a peak ofblood flow or volume in a cardiac cycle, the reflected/transmitted lightI_(L) is at a minimum due to absorption by the increased blood volume,e.g., due to the pulsating blood in the vessel. At a minimum of bloodvolume during the cardiac cycle, the transmitted/reflected light I_(H)416 is at a maximum due to lack of absorption from the pulsating blood.

The biosensor 100 is configured to filter the reflected/transmittedlight I_(L) of the pulsating blood from the transmitted/reflected lightI_(H). This filtering isolates the light due to reflection/transmissionof the pulsating blood from the light due to reflection/transmissionfrom non-pulsating blood, vessel walls, surrounding tissue, etc. Thebiosensor 100 may then measure the concentration levels of one or moresubstances from the reflected/transmitted light I_(L) 814 in thepulsating blood.

For example, incident light I_(O) is directed at a tissue site at one ormore wavelengths. The reflected/transmitted light I is detected by aphotodetector or sensor array in a camera. At a peak of blood flow orvolume, the reflected light I_(L) 414 is at a minimum due to absorptionby the pulsating blood, non-pulsating blood, other tissue, etc. At aminimum of blood flow or volume during the cardiac cycle, the Incidentor reflected light I_(H) 416 is at a maximum due to lack of absorptionfrom the pulsating blood volume. Since the light I is reflected ortraverses through a different volume of blood at the two measurementtimes, the measurement provided by a PPG sensor is said to be a‘volumetric measurement’ descriptive of the differential volumes ofblood present at a certain location within the user's vessels atdifferent times during the cardiac cycle. These principles describedherein may be applied to venous blood flow and arterial blood flow.

In general, the relative magnitudes of the AC and DC contributions tothe reflected/transmitted light signal I may be determined. In general,AC contribution of the reflected light signal I is due to the pulsatingblood flow. A difference function may thus be computed to determine therelative magnitudes of the AC and DC components of the reflected light Ito determine the magnitude of the reflected light due to the pulsatingblood flow. The described techniques herein for determining the relativemagnitudes of the AC and DC contributions is not intended as limiting.It will be appreciated that other methods may be employed to isolate orotherwise determine the relative magnitude of the light I_(L) due topulsating blood flow (arterial and/or venous).

In one aspect, the spectral response obtained at each wavelength may bealigned based on the systolic 402 and diastolic 404 points in theirrespective spectral responses. This alignment is useful to associateeach spectral response with a particular stage or phase of thepulse-induced local pressure wave within the blood vessel (which roughlymimics the cardiac cycle 406 and thus include systolic and diastolicstages and sub-stages thereof). This temporal alignment helps todetermine the absorption measurements acquired near a systolic point intime of the cardiac cycle and near the diastolic point in time of thecardiac cycle 406 associated with the local pressure wave within theuser's blood vessels. This measured local pulse timing information maybe useful for properly interpreting the absorption measurements in orderto determine the relative contributions of the AC and DC componentsmeasured by the biosensor 100. So, for one or more wavelengths, thesystolic points 402 and diastolic points 404 in the spectral responseare determined. These systolic points 402 and diastolic points 404 forthe one or more wavelengths may then be aligned as a method to discernconcurrent responses across the one or more wavelengths.

In another embodiment, the systolic points 402 and diastolic points 404in the absorbance measurements are temporally correlated to thepulse-driven pressure wave within the blood vessels—which may differfrom the cardiac cycle. In another embodiment, the biosensor 100 mayconcurrently measure the intensity reflected at each the plurality ofwavelengths. Since the measurements are concurrent, no alignment of thespectral responses of the plurality of wavelengths may be necessary.FIG. 4 illustrates the spectral response obtained at the plurality ofwavelengths with the systolic points 402 and diastolic points 404aligned over a cardiac cycle 406.

FIG. 5 illustrates a logical flow diagram of an embodiment of a method500 of the biosensor 100. In one aspect, the biosensor 100 emits anddetects light at a plurality of predetermined frequencies orwavelengths, such as approximately 940 nm, 660 nm, 390 nm, 592 nm, and468 nm or in ranges thereof. The light is pulsed for a predeterminedperiod of time (such as 100 usec or 200 Hz) sequentially orsimultaneously at each predetermined wavelength. In another aspect,light may be pulsed in a wavelength range of 1 nm to 50 nm around eachof the predetermined wavelengths. For example, for the predeterminedwavelength 390 nm, the biosensor 100 may transmit light directed at skintissue of the user in a range of 360 nm to 410 nm including thepredetermined wavelength 390 nm. For the predetermined wavelength of 940nm, the biosensor 100 may transmit light directed at the skin tissue ofthe user in a range of 920 nm to 975 nm. In another embodiment, thelight is pulsed simultaneously at least at each of the predeterminedwavelengths (and in a range around the wavelengths).

The spectral responses are obtained around the plurality of wavelengths,including at least a first wavelength and a second wavelength at 502.The spectral responses may be measured over a predetermined period (suchas 300 usec.) or at least over 2-3 cardiac cycles. This measurementprocess is repeated continuously, e.g., pulsing the light at 10-100 Hzand obtaining spectral responses over a desired measurement period, e.g.from 1-2 seconds to 1-2 minutes or from 2-3 hours to continuously overdays or weeks. The spectral data obtained by the PPG circuit 110, suchas the digital or analog spectral responses, may be processed locally bythe biosensor 100 or transmitted to a central control module forprocessing.

The systolic and diastolic points of the spectral response are thendetermined. Because the human pulse is typically on the order ofmagnitude of one 1 Hz, typically the time differences between thesystolic and diastolic points are on the order of magnitude ofmilliseconds or tens of milliseconds or hundreds of milliseconds. Thus,spectral response measurements may be obtained at a frequency of around10-100 Hz over the desired measurement period. The spectral responsesare obtained over one or more cardiac cycles and systolic and diastolicpoints of the spectral responses are determined. Preferably, thespectral response is obtained over at least three cardiac cycles inorder to obtain a heart rate.

A low pass filter (such as a 5 Hz low pass filter) is applied to thespectral response signal at 504. The relative contributions of the ACand DC components are obtained I_(AC+DC) and I_(AC). A peak detectionalgorithm is applied to determine the systolic and diastolic points at506. If not detected concurrently, the systolic and diastolic points ofthe spectral response for each of the wavelengths may be aligned or maybe aligned with systolic and diastolic points of a pressure pulsewaveform or cardiac cycle.

Beer Lambert equations are then applied as described herein. Forexample, the L_(λ), values are then calculated for the first wavelengthλ₁ at 508 and the second wavelength λ₂ at 510, wherein the L_(λ) valuesfor a wavelength equals:

$L_{\lambda} = {{Log}\; 10\mspace{11mu} \left( \frac{{IAC} + {DC}}{IDC} \right)}$

wherein I_(AC+DC) is the intensity of the detected light with AC and DCcomponents and I_(DC) is the intensity of the detected light with the ACcomponent filtered by the low pass filter. The value L_(λ) isolates thespectral response due to pulsating arterial blood flow, e.g. the ACcomponent of the spectral response.

A ratio R of the L_(λ) values at two wavelengths may then be determinedat 512. For example, the ratio R may be obtained from the following:

${{Ratio}\mspace{14mu} R} = \frac{L\lambda 1}{L\lambda 2}$

The spectral responses may be measured and the L_(λ) values and Ratio Rdetermined continuously, e.g. every 1-2 seconds, and the obtained L_(λ)values and/or Ratio R averaged over a predetermined time period, such asover 1-2 minutes. The concentration level of a substance may then beobtained from the R value and a calibration database at 514. Thebiosensor 100 may continuously monitor a user over 2-3 hours orcontinuously over days or weeks.

In one embodiment, the R_(390,940) value with L_(λ1=390nm) andL_(λ2=940) may be non-invasively and quickly and easily obtained usingthe biosensor 100 to determine a concentration level of nitric oxide NOin blood flow of a user. In particular, in unexpected results, it isbelieved that the nitric oxide NO levels in the blood flow is beingmeasured at least in part by the biosensor 100 at wavelengths in therange of 380-410 and in particular at λ₁=390 nm. Thus, the biosensor 100measurements to determine the L_(390nm) values are the first time NOconcentration levels in arterial blood flow have been measured directlyin vivo. These and other aspects of the biosensor 100 are described inmore detail herein with clinical trial results.

Embodiment—Determination of Concentration Level of a Substance Using PPGSignals at a Plurality of Wavelengths

FIG. 6 illustrates a logical flow diagram of an exemplary method 600 todetermine levels of a substance in blood flow using the PPG signals at aplurality of wavelengths. The absorption coefficient of a substance maybe sufficiently higher at a plurality of wavelengths, e.g. due toisoforms or derivative compounds. For example, the increased intensityof light at a plurality of wavelengths may be due to reflectance byisoforms or other compounds in the arterial blood flow. Another methodfor determining the concentration levels may then be used by measuringthe spectral responses and determining L and R values at a plurality ofdifferent wavelengths of light. In this example then, the concentrationlevel of the substance is determined using spectral responses atmultiple wavelengths. An example for calculating the concentration of asubstance over multiple wavelengths may be performed using a linearfunction, such as is illustrated herein below.

LN(I _(1-n))=Σ_(i=0) ^(n) μi*Ci

wherein,

I_(1-n)=intensity of light at wavelengths λ_(1-n)

μ_(n)=absorption coefficient of substance 1, 2, . . . n at wavelengthsλ_(1-n)

C_(n)=Concentration level of substance 1, 2, . . . n

When the absorption coefficients μ_(1-n) of a substance, its isoforms orother compounds including the substance are known at the wavelengthsthen the concentration level C of the substances may be determined fromthe spectral responses at the wavelengths λ_(1-n) (and e.g., including arange of 1 nm to 50 nm around each of the wavelengths). Theconcentration level of the substance may be isolated from the isoformsor other compounds by compensating for the concentration of thecompounds. Thus, using the spectral responses at multiple frequenciesprovides a more robust determination of the concentration level of asubstance.

In use, the biosensor 100 transmits light directed at skin tissue at aplurality of wavelengths or over a broad spectrum at 602. The spectralresponse of light from the skin tissue is detected at 604, and thespectral responses are analyzed at a plurality of wavelengths (and inone aspect including a range of +/−10 to 50 nm around each of thewavelengths) at 606. Then, the concentration level C of the substancemay be determined using the spectral responses at the plurality ofwavelengths at 608. The concentration level of the substance may beisolated from isoforms or other compounds by compensating for theconcentration of the compounds. For example, using absorptioncoefficients for Nitric Oxide and Hemoglobin, the concentration ofNitric Oxide can be obtained in arterial blood. A calibration tableusing human subjects may then to correlate amounts of glucose (mG/DL) inrelation to R values (NoHb) 404/940 nm.

FIG. 7 illustrates a logical flow diagram of an exemplary method 700 todetermine levels of a substance using the spectral responses at aplurality of wavelengths in more detail. The spectral responses areobtained at 702. The spectral response signals include AC and DCcomponents I_(AC+DC). A low pass filter (such as a 5 Hz low pass filter)is applied to each of the spectral response signals I_(AC+DC) to isolatethe DC component of each of the spectral response signals I_(DC) at 704.The AC fluctuation is due to the pulsatile expansion of the vessels dueto the volume increase in pulsating blood. In order to measure the ACfluctuation, measurements are taken at different times and a peakdetection algorithm is used to determine the diastolic point and thesystolic point of the spectral responses at 706. A Fast Fouriertransform (FFT) algorithm may also be used to isolate the DC componentI_(DC) and AC component of each spectral response signal at 706. Adifferential absorption technique may also be used as described in moredetail herein. The I_(DC) component is thus isolated from the spectralsignal at 708.

The T_(AC+DC) and I_(DC) components are then used to compute the Lvalues at 710. For example, a logarithmic function may be applied to theratio of I_(AC+DC) and I_(DC) to obtain an L value for each of thewavelengths L_(λ1-n). Since the respiratory cycle affects the PPGsignals, the L values may be averaged over a respiratory cycle and/orover another predetermined time period (such as over a 1-2 minute timeperiod) or over a plurality of cardiac cycles at 712.

In an embodiment, isoforms of a substance may be attached in the bloodstream to one or more types of hemoglobin compounds. The concentrationlevel of the hemoglobin compounds may then need to be accounted for toisolate the concentration level of the substance from the hemoglobincompounds. For example, nitric oxide (NO) is found in the blood streamin a gaseous form and also attached to hemoglobin compounds. Thus, thespectral responses obtained around 390 nm (+/−20 nm) may include aconcentration level of the hemoglobin compounds as well as nitric oxide.The hemoglobin compound concentration levels must thus be compensatedfor to isolate the nitric oxide concentration levels. Multiplewavelengths and absorption coefficients for hemoglobin are used todetermine a concentration of the hemoglobin compounds at 714. Othermethods may also be used to obtain a concentration level of hemoglobinin the blood flow as well. The concentration of the hemoglobin compoundsis then adjusted from the measurements at 716. The concentration valuesof the substance may then be obtained at 718. For example, the R valuesare then determined at 718.

To determine a concentration level of the substance, a calibration tableor database is used that associates the obtained R value to aconcentration level of the substance at 720. The calibration databasecorrelates the R value with a concentration level. The calibrationdatabase may be generated for a specific user or may be generated fromclinical data of a large sample population. For example, it isdetermined that the R values should correlate to similar NOconcentration levels across a large sample population. Thus, thecalibration database may be generated from testing of a large sample ofa general population to associate R values and NO concentration levels.

In addition, the R values may vary depending on various factors, such asunderlying skin tissue. For example, the R values may vary for spectralresponses obtained from an abdominal area versus measurements from awrist or finger due to the varying tissue characteristics. Thecalibration database may thus provide different correlations between theR values and concentration levels of a substance depending on theunderlying skin tissue characteristics. The concentration level of thesubstance in blood flow is then obtained using the calibration table at722. The concentration level may be expressed as mmol/liter, as asaturation level percentage, as a relative level on a scale, etc.

Embodiment—Determination of Concentration Levels of a Substance UsingShifts in Absorbance Peaks

In another embodiment, a concentration level of a substance may beobtained from measuring a characteristic shift in an absorbance peak ofhemoglobin. For example, the absorbance peak for methemoglobin shiftsfrom around 433 nm to 406 nm in the presence of NO. The advantage of themeasurement of NO by monitoring methemoglobin production includes thewide availability of spectrophotometers, avoidance of sampleacidification, and the relative stability of methemoglobin. Furthermore,as the reduced hemoglobin is present from the beginning of anexperiment, NO synthesis can be measured continuously, removing theuncertainty as to when to sample for NO.

The biosensor 100 may detect nitric oxide in vivo using PPG techniquesby measuring the shift in the absorbance spectra curve of reducedhemoglobin in tissue and/or arterial blood flow. The absorbance spectracurve shifts with a peak from around 430 nm to a peak around 411 nmdepending on the production of methemoglobin. The greater the degree ofthe shift of the peak of the curve, the higher the production ofmethemoglobin and NO concentration level. Correlations may be determinedbetween the degree of the measured shift in the absorbance spectra curveof reduced hemoglobin to a concentration level of NO. The correlationsmay be determined from a large sample population or for a particularuser and stored in a calibration database. The biosensor 100 may thusobtain an NO concentration level by measuring the shift of theabsorbance spectra curve of reduced hemoglobin. A similar method ofdetermining shifts in absorbance spectra may be implemented to determinea blood concentration level of other substances.

The biosensor 100 may obtain an NO concentration level by measuring theshift of the absorbance spectra curve of deoxygenated hemoglobin and/orby measuring the shift of the absorbance spectra curve of oxygenatedhemoglobin in vivo. The biosensor 100 may then access a calibrationdatabase that correlates the measured shift in the absorbance spectracurve of deoxygenated hemoglobin to an NO concentration level.Similarly, the biosensor may access a calibration database thatcorrelates the measured shift in the absorbance spectra curve ofoxygenated hemoglobin to an NO concentration level.

FIG. 8 illustrates a logical flow diagram of an exemplary embodiment ofa method 800 for measuring a concentration level of a substance in vivousing shifts in absorbance spectra. The biosensor 100 may obtain aconcentration of the substance by measuring shifts in absorbance spectraof one or more substances that interact with the substance. For example,the one or more substances may include oxygenated and deoxygenatedhemoglobin (HB). The PPG circuit 110 detects PPG signals at a pluralityof wavelengths with a high absorption coefficient of the one or moresubstances that interact with the substance at 802. The biosensor 100determines the relative shift in the absorbance spectra for thesubstance at 804. For example, the biosensor 100 may measure theabsorbance spectra curve of deoxygenated HB and determine its relativeshift or peak between the range of approximately 430 nm and 405 nm. Inanother example, the biosensor 100 may measure the absorbance spectracurve of oxygenated HB and determine its relative shift or peak between421 nm and 393 nm.

The biosensor 100 accesses a calibration database that correlates therelative shift in the absorbance spectra of the substance with aconcentration level of the substance at 806. The biosensor 100 may thusobtain a concentration level of the substance in blood flow using acalibration database and the measured relative shift in absorbancespectra at 808.

The various methods thus include one or more of: Peak & Valley (e.g.,peak detection), FFT, and differential absorption. Each of the methodsrequire different amounts of computational time which affects overallembedded computing time for each signal, and therefore can be optimizedand selectively validated with empirical data through large clinicalsample studies. The biosensor 100 may use a plurality of these methodsto determine a plurality of values for the concentration level of thesubstance. The biosensor 100 may determine a final concentration valueusing the plurality of values. For example, the biosensor 100 mayaverage the values, obtain a mean of the values, etc.

The biosensor 100 may be configured for measurement on a fingertip orpalm, wrist, an arm, forehead, chest, abdominal area, ear lobe, or otherarea of the skin or body or living tissue. The characteristics ofunderlying tissue vary depending on the area of the body, e.g. theunderlying tissue of an abdominal area has different characteristicsthan the underlying tissue at a wrist. The operation of the biosensor100 may need to be adjusted in response to its positioning due to suchvarying characteristics of the underlying tissue. The PPG circuit 110may adjust a power of the LEDs or a frequency or wavelength of the LEDsbased on the underlying tissue. The biosensor 100 may adjust processingof the data. For example, an absorption coefficient may be adjusted whendetermining a concentration level of a substance based on Beer-Lambertprinciples due to the characteristics of the underlying tissue.

In addition, the calibrations utilized by the biosensor 100 may varydepending on the positioning of the biosensor. For example, thecalibration database may include different table or other correlationsbetween R values and concentration level of a substance depending onposition of the biosensor. Due to the different density of tissue andvessels, the R value obtained from measurements over an abdominal areamay be different than measurements over a wrist or forehead orfingertip. The calibration database may thus include differentcorrelations of the R value and concentration level depending on theunderlying tissue. Other adjustments may also be implemented in thebiosensor 100 depending on predetermined or measured characteristics ofthe underlying tissue of the body part.

Embodiment—Respiration Rate, Heart Rate and Pulse Pressure

FIG. 9 illustrates a schematic drawing of an exemplary embodiment of aPPG Signal 900 obtained using an embodiment of the biosensor 100 from auser. The PPG Signal 900 was obtained at a wavelength of around 395 nmand is illustrated for a time period of about 40 seconds. The PPG Signal900 was filtered using digital signal processing techniques to eliminatenoise and background interference to obtain the filtered PPG Signal 900.A first respiration cycle 902 and a second respiration cycle 904 may beobtained by measuring a low frequency component or fluctuation of thefiltered PPG Signal 900. From this low frequency component, thebiosensor 100 may obtain a respiratory rate of a user from the PPGSignal 900.

A heart rate may be determined from the spectral response. For example,the biosensor 100 may determine the time between diastolic points orbetween systolic points to determine a time period of a cardiac cycle906. In another embodiment, to estimate the heart rate, the frequencyspectrum of the PPG signal is obtained using a FFT algorithm over apredetermined period (hamming window). The pulse rate is estimated asthe frequency that corresponds to the highest power in the estimatedfrequency spectrum. The frequency spectrum may be averaged over a timeperiod, such as a 5-10 second window.

A pulse pressure 908 may be determined from the PPG signal 900. Thepulse pressure 908 corresponds to an amplitude of the PPG signal 900 ora peak to peak value. The amplitude of the PPG signal 900 may beaveraged over a time period to determine a pulse pressure 908.

Thus, a PPG signal may be used to determine heart rate, respiration rateand pulse rate. A light source in the UV range provides a PPG signalwith a lower signal to noise ratio for determining heart rate andrespiration rate in some tissue while a light source in the IR rangeprovides a PPG signal with a lower signal to noise ratio in other typesof tissue. The infrared range (IR) range may include wavelengths from650 nm to 1350 nm.

FIG. 10 illustrates a schematic drawing of an exemplary embodiment ofresults of R values 1000 determined using a plurality of methods. The Rvalues 1000 corresponding to the wavelengths of 395 nm/940 nm isdetermined using three methods. The R Peak Valley curve 1002 isdetermined using the Ratio

$R = \frac{L395}{L940}$

as described hereinabove. The R FFT curve 1004 is obtained using FFTtechniques to determine the I_(DC) values and I_(AC) component values ofthe spectral responses to determine the Ratio

$R = {\frac{L395}{L940}.}$

The R differential absorption curve 1006 is determined using the shiftin absorbance spectra as described in more detail in U.S. Utilityapplication Ser. No. 15/275,388 entitled, “SYSTEM AND METHOD FOR HEALTHMONITORING USING A NON-INVASIVE, MULTI-BAND BIOSENSOR,” filed Sep. 24,2016, now U.S. Pat. No. 9,642,578 issued May 9, 2017, and herebyexpressly incorporated by reference herein.

As seen in FIG. 10, the determination of the R values using the threemethods provides similar results, especially when averaged over a periodof time. A mean or average of the R values 1002, 1004 and 1006 may becalculated to obtain a final R value or one of the methods may bepreferred depending on the positioning of the biosensor or underlyingtissue characteristics.

FIG. 11A illustrates a schematic drawing of an exemplary embodiment ofan empirical calibration curve 1100 for correlating oxygen saturationlevels (SpO₂) with R values. The calibration curve 1100 may be includedas part of the calibration database for the biosensor 100. For example,the R values may be obtained for L_(660nm)/L_(940nm). In one embodiment,the biosensor 100 may use a light source in the 660 nm wavelength or ina range of +/−50 nm to determine SpO₂ levels, e.g. rather than a lightsource in the IR wavelength range. The 660 nm wavelength has beendetermined in unexpected results to have good results in measuringoxygenated hemoglobin, especially in skin tissue with fatty deposits,such as around the abdominal area.

FIG. 11B illustrates a schematic drawing of an exemplary embodiment ofan empirical calibration curve 1102 for correlating NO levels (mg/dl)with R values. The calibration curve 1102 may be included as part of thecalibration database for the biosensor 100. For example, the R valuesmay be obtained in clinical trials from measurements ofL_(395nm)/L_(940nm) and the NO levels of a general sample population.The NO levels may be measured using one or more other techniques forverification to generate such a calibration curve 1102. This embodimentof the calibration curve 1102 is based on limited clinical data and isfor example only. Additional or alternative calibration curves 1212 mayalso be derived from measurements of a general population of users atone or more different positions of the biosensor 100. For example, afirst calibration curve may be obtained at a forehead, another for anabdominal area, another for a fingertip, another for a palm, etc.

From the clinical trials, the L values obtained at wavelengths around390 nm (e.g. 380-410) are measuring nitric level (NO) levels in thearterial blood flow. The R value for L390/L940 nm may thus be used toobtain NO levels in the pulsating blood flow. From the clinical trials,it seems that the NO levels are reflected in the R values obtained fromL390 nm/L940 nm and wavelengths around 390 nm such as L395 nm/L940 nm.The NO levels may thus be obtained from the R values and a calibrationdatabase that correlates the R value with known concentration levels ofNO.

In other embodiments, rather than Lλ1=390 nm, the L value may bemeasured at wavelengths in a range from 410 nm to 380 nm, e.g., as seenin the graphs wherein Lλ1=395 nm is used to obtain a concentration levelof NO. In addition, Lλ2 may be obtained at any wavelength atapproximately 660 nm or above. Thus, R obtained at approximately Lλ1=380nm-400 nm and Lλ2>660 nm may also be obtained to determine concentrationlevels of NO.

In an embodiment, the concentration level of NO may be correlated to adiabetic risk or to blood glucose levels using a calibration database.For example, the R value is averaged over a short period of time (e.g.,around less than 2-3 minutes) and then correlated with a level ofglucose.

Embodiment—Detection of a Risk of Sepsis or an Infection Based on NOLevels

In an embodiment, the biosensor 100 may detect a risk of sepsis using NOconcentration levels. In this embodiment, an R value derived from L₃₉₅and L₉₄₀ is used to determine a NO measurement though other parametersmay be obtained, such as R_(390/940) or L₃₉₀. In the clinical trialsherein, the R_(395/940) value for a person without a sepsis conditionwas in a range of 0.1-8. In addition, it was determined that theR_(395/940) value of 30 or higher is indicative of a patient with asepsis condition and that the R_(395/940) value of 8-30 was indicativeof a risk of sepsis in the patient. In general, the R_(395/940) value of2-3 times a baseline of the R_(395/940) value was indicative of a riskof sepsis in the patient. These ranges are based on preliminary clinicaldata and may vary. In addition, a position of the biosensor,pre-existing conditions of a patient or other factor may alter thenumerical values of the ranges of the R_(395/940) values describedherein.

The R values are determined by using a wavelength in the UV range withhigh absorption coefficient for NO, e.g. in a range of 380 nm-410 nm.These R values have a large dynamic range from 0.1 to 300 and above. Thepercentage variance of R values in these measurements is from 0% to over3,000%. The R values obtained by the biosensor 100 are thus moresensitive and may provide an earlier detection of septic conditions thanblood tests for serum lactate or measurements based on MetHb.

For example, an optical measurement of MetHb in blood vessels is in arange of 0.8-2. This range has a difference of 1.1 to 1.2 between anormal value and a value indicating a septic risk. So, thesemeasurements based on MetHb have less than a 1% percentage variance. Inaddition, during a septic condition, MetHb may become saturated due tothe large amount of NO in the blood vessels. So, an optical measurementof MetHb alone or other hemoglobin species alone is not able to measurethese excess saturated NO levels. The R values determined by measuringNO level directly using a wavelength in the UV range are thus moresensitive, accurate, have a greater dynamic range and variance, andprovide an earlier detection of septic conditions.

A baseline NO measurement in blood vessels of a healthy generalpopulation is obtained. For example, the biosensor 100 may obtain Rvalues or other NO measurements using the biosensor 100. For example,the biosensor 100 may measure an L395 value or determine SpNO % based onan R value for a general population over a period of time, such as hoursor days. These NO measurements are then averaged to determine a baselineNO measurement. The NO measurement in blood vessels is then obtained fora general population with a diagnosis of sepsis. For example, thebiosensor 100 may obtain R values or other NO measurements (such as anL395 value or SpNO %) for patients diagnosed with sepsis usingtraditional blood tests, such as serum lactate blood tests. Thebiosensor 100 may monitor the patients throughout the diagnosis andtreatment stages. The NO measurements are then averaged to determine arange of values that indicate a septic condition.

Predetermined thresholds may then be obtained from the NO measurements.For example, a threshold value indicative of a non-septic condition maybe obtained. A threshold value for a septic condition may also beobtained. The biosensor 100 is then configured with the predeterminedthresholds for the NO measurement.

The predetermined thresholds may be adjusted based on an individualpatient's pre-existing conditions. For example, a patient with diabetesmay have lower R values. A baseline NO value for a patient may also bedetermined based on monitoring of the patient during periods withoutinfections. The predetermined thresholds stored in the bio sensor 100may then be adjusted based on any individual monitoring and/orpre-existing conditions.

In addition, the predetermined thresholds may be determined and adjustedbased on positioning of the biosensor 100. For example, different Rvalues or other NO measurements may be obtained depending on thecharacteristics of the underlying tissue, such as tissue with high fattydeposits or with dense arterial blood flow. The thresholds and otherconfigurations of the biosensor 100 may thus be adjusted depending onthe underlying skin tissue, such as a forehead, chest, arm, leg, finger,abdomen, etc.

Embodiment—Detection of Other Conditions Based on NO Levels

In another embodiment, post-traumatic stress disorder (PTSD) may resultin higher than normal NO levels. There are several reports thatincreased oxidative stress may be a factor in the evolution of someenduring neurological and psychiatric disorders and PTSD. Stress, a riskfactor for developing PTSD, evokes a sustained increase in nitric oxidesynthase (NOS) activity that can generate excessive amounts of nitricoxide. Oxidation of nitric oxide produces peroxynitrite that is verytoxic to nerve cells, and elevated levels of peroxynitrite and itsprecursor nitric oxide have been observed in patients with PTSD. Anarticle by Kedar N. Prasad and Stephen C. Bondy, entitled, “Commonbiochemical defects linkage between post-traumatic stress disorders,mild traumatic brain injury (TBI) and penetrating TBI,” Brain Research,Volume 1599, Pages 103-114, Mar. 2, 2015, and incorporated by referenceherein, describes the elevation of nitric oxide NO that may indicatePTSD. The biosensor 100 may thus operate in one or more modes to detector provide a warning of abnormal NO levels indicative of PTSD.

In another embodiment, concussions, mild traumatic brain injury (TBI)and penetrating TBI, may also result in abnormal NO levels. The articleby James H. Silver, entitled, “Inorganic Nitrite as a Potential Therapyor Biomarker for Concussion,” J. Neurol Neurophysiol, Volume 7, Issue 2(April 2016), and incorporated by reference herein, describes anabnormal pattern of nitric oxide NO levels after a concussion. Forexample, it has been observed that a rapid increase in nitric oxideoccurs within minutes following head injury, followed by a decline tobelow baseline within hours. The biosensor 100 may monitor NO levelsafter a head trauma and detect this sudden increase and then reductionbelow baseline in NO levels. In use, a baseline level of NO may bedetermined for a user during normal conditions. After a potential headinjury, the user is then monitored by the biosensor 100 for changes fromthis baseline level of NO. This process may be performed, e.g., forsideline evaluation of potentially concussed athletes. Thus, thebiosensor 100 may operate in one or more modes to monitor NO levels andprovide a warning of abnormal NO levels that may indicate a concussionor TBI.

In one or more modes of operation, the biosensor 100 may thus beconfigured to detect one or more of these other substances in additionto or alternatively from NO levels in blood flow.

FIG. 12 illustrates a schematic block diagram of an embodiment of acalibration database 1200. The calibration database 1200 includes one ormore calibration tables 1202, calibration curves 1204 or calibrationfunctions 1206 for correlating obtained values to concentration levelsof one or more substances A-N. The concentration level of the substancesmay be expressed in the calibration tables 1202 as units of mmol/liter,as a saturation level percentage (SpNO %), as a relative level on ascale (e.g., 0-10), etc.

The calibration database 1200 may also include one or more calibrationtables for one or more underlying skin tissue types. In one aspect, thecalibration database 1200 may correlate an R value to a concentrationlevel of a substance for a plurality of underlying skin tissue types.

In another aspect, a set of calibration tables 1202 may correlate anabsorption spectra shift to a concentration level of one or moresubstances A-N. For example, a first table may correlate a degree ofabsorption spectra shift of oxygenated hemoglobin to NO concentrationlevels. The degree of shift may be for the peak of the absorbancespectra curve of oxygenated hemoglobin from around 421 nm. In anotherexample, the set of table 1202 may correlate a degree of absorptionspectra shift of deoxygenated hemoglobin to NO concentration levels. Thedegree of shift may be for the peak of the absorbance spectra curve ofdeoxygenated hemoglobin from around 430 nm.

The calibration database 1200 may also include a set of calibrationcurves 1204 for a plurality of substances A-N. The calibration curvesmay correlate L values or R values or degree of shifts of spectral datato concentration levels of the substances A-N.

The calibration database 1200 may also include calibration functions1206. The calibration functions 1206 may be derived (e.g., usingregressive functions) from the correlation data from the calibrationcurves 1204 or the calibration tables 1202. The calibration functions1206 may correlate L values or R values or degree of shifts in spectraldata to concentration levels of the substances A-N for one or moreunderlying skin tissue types.

Embodiment—Neural Network

One or more types of artificial neural networks (a.k.a. machine learningalgorithms) may be implemented herein to determine health data from PPGsignals. For example, neural networks may be used to obtain aconcentration level of NO or glucose or other health data from inputdata derived from PPG signals. Neural network models can be viewed assimple mathematical models defining a function ƒ wherein ƒ:X→Y or adistribution over X or both X and Y. Types of neural network engines orAPIs currently available include, e.g. TensorFlow™, Keras™, Microsoft®CNTK™, Caffe™, Theano™ and Lasagne™.

Sometimes the various machine learning techniques are intimatelyassociated with a particular learning rule. The function ƒ may be adefinition of a class of functions (where members of the class areobtained by varying parameters, connection weights, thresholds, etc.).The neural network learns by adjusting its parameters, weights andthresholds iteratively to yield desired output. The training isperformed using defined set of rules also known as the learningalgorithm. Machine learning techniques include ridge linear regression,a multilayer perceptron neural network, support vector machines andrandom forests. For example, a gradient descent training algorithm isused in case of supervised training model. In case, the actual output isdifferent from target output, the difference or error is determined. Thegradient descent algorithm changes the weights of the network in such amanner to minimize this error. Other learning algorithms include backpropagation, least mean square (LMS) algorithm, etc. A set of examplesor a training set is used for learning by the neural network. Thetraining set is used to identify the parameters [e.g., weights] of thenetwork.

FIG. 13 illustrates a logical flow diagram of an embodiment of a method1300 for using a machine learning neural network technique for detectionof health data. In an embodiment, patient data is obtained at 1302. Thepatient data may include one or more of: age, weight, body mass index,temperature, blood pressure, pre-existing medical conditions, traumaevents, mental conditions, injuries, demographic data, physicalexaminations, laboratory tests, diagnosis, treatment procedures,prescriptions, radiology examinations, historic pathology, medicalhistory, surgeries, etc. PPG signals at one or more wavelengths areobtained at 1304.

Various parameters of the PPG signals may be determined or measured at1306 These parameters include the diastolic and systolic points,transfer functions, timing differences between wavelengths, the Lvalues, R values, pulse shape (measured by autoregression coefficientsand moving averages), characteristic features of the shape of the PPGwaveform, the average distance between pulses, variance, instant energyinformation, energy variance, etc. Other parameters may be extracted byrepresenting the PPG signal as a stochastic auto-regressive movingaverage (ARMA). Parameters also may be extracted by modeling the energyof the PPG signal using the Teager-Kaiser operator, calculating theheart rate and cardiac synchrony of the PPG signal, and determining thezero crossings of the PPG signal. These and other parameters may beobtained using a PPG signal. The PPG input data may include the PPGsignals, and/or one or more parameters derived from the PPG signals.

An input vector is obtained at 1308. The input vector includes the PPGinput data, such as the PPG signals at one or more wavelengths and/orone or more parameters generated from the PPG signals at the one or morewavelengths. Since the PPG signal is of variable duration, a fixeddimension vector for a measurement of the PPG signal may be obtained.The input vector may also include patient data.

The input vector is processed by a processing device executing a neuralnetwork (aka machine learning algorithm). The processing device executesthe machine learning algorithm or neural network techniques using theinput vector to determine health data at 1310. The health data includesone or more of heart rate, period of vasodilation, level ofvasodilation, respiration rate, blood pressure, oxygen saturation level,NO level, liver enzyme level, Glucose level, Blood alcohol level, bloodtype, sepsis risk factor, infection risk factor, cancer, virusdetection, creatinine level or electrolyte level. The health data mayalso include blood viscosity, blood pressure, arterial stiffness,vascular health, cardiovascular risk, atherosclerosis, etc. The healthdata may be generated as an output fixed length vector.

The obtained health data may be compared to expected ranges orthresholds in a calibration table at 1312. Alarms or warnings may beissued based on the comparison.

Embodiment—Measurement of Vasodilation Using PPG Signals

Vasodilation is the widening of blood vessels. It results fromrelaxation of smooth muscle cells within the vessel walls, in particularin the large veins, large arteries, and smaller arterioles. The processis the opposite of vasoconstriction, which is the narrowing of bloodvessels due to constriction of the smooth muscle cells within thevessels walls. The vascular endothelium is crucially involved in thefundamental regulation of blood flow matching demand and supply oftissue. After transient ischemia, arterial inflow increases. As aresponse to increased shear forces during reactive hyperemia, healthyarteries dilate via release of NO or other endothelium-derivedvasoactive substances. This endothelium-dependent flow-mediatedvasodilation (FMD) is impaired in atherosclerosis.

The capacity of blood vessels to respond to physical and chemicalstimuli in the lumen confers the ability to self-regulate tone and toadjust blood flow and distribution in response to changes in the localenvironment. Many blood vessels respond to an increase in flow, or moreprecisely shear stress, by dilating. This phenomenon is designatedflow-mediated vasodilation (FMD). A principal mediator of FMD isendothelium-derived NO—an example of an EDRF.

Although the precise mechanism by which vasodilation occurs duringreactive hyperemia in FMD measurement has not been fully elucidated,nitric oxide (NO) has been proposed as a principal mediator of FMD. TheNO, produced as a result of an increase of endothelial NO synthaseactivity induced by shear stress, diffuses into the tunica media,leading to relaxation of smooth muscle cells and subsequentvasodilation. The assessment of endothelial function by FMD, therefore,presupposes a normal structural condition. Impairedendothelium-independent vasodilation is thought to be associated withstructural vascular alterations and alterations in smooth muscle cells,e.g. as a result of atherosclerosis.

As the presence of endothelial dysfunction is closely associated withcardiovascular risk and outcome, the measurement of FMD in the brachialartery has become a standard method for the assessment of endothelialfunction in patients and to evaluate therapeutic interventions targetingatherosclerosis. For example, in healthy humans, the relative increasein brachial artery diameter during vasodilation is typically in the 5%to 10% range.

The current measurement of FMD in the brachial artery requires usinghigh-frequency ultrasound to visually inspect the brachial artery. Forexample, one process includes applying a stimulus to provoke theendothelium to release nitric oxide (NO) with subsequent vasodilationthat is then imaged and quantitated as an index of vasomotor function.This process of high-resolution ultrasonography of the brachial arteryto evaluate FMD has limitations. It must be performed in a clinicalsetting by a medical clinician using expensive ultrasonographyequipment.

Thus, there is a need for an improved system and method for detection ofvasodilation and conditions affected by vasodilation and conditions thataffect vasodilation. The systems and methods used to describe detectionof a level of vasodilation and periods of vasodilation may be used todetermine vasoconstriction.

In various embodiments described herein, vasodilation orvasoconstriction and characteristics thereof may be measured using PPGsignals obtained by the biosensor. The effects of vasodilation may beobserved in PPG signals in one or more of a plurality of wavelengthsacross different spectrums, such as IR, visible and UV. For example, PPGsignals across the spectrum may vary in shape, intensity level andtiming due to vasodilation. In one example, the effect of vasodilationis observed from phase differences between PPG signals of differentwavelengths, especially between wavelengths in different spectrums.Vasodilation also causes subtle skin movement which may be observed inPPG signals, especially in lower frequency components of PPG signals(e.g. frequencies that do not reflect the pulsatile blow flow). Usingone or more characteristics of the PPG signals, a level of vasodilationmay be obtained. The level of vasodilation may be measured as apercentage change in the size of vessels, such as percentage increase ina baseline diameter or planar area, or in a range such as 1-10, or inother manners.

In addition, an arterial stiffness or elasticity index may be obtainedusing the PPG signals. The PPG signals may predict vascular health, suchas atherosclerosis. For example, a timing or period to change from astate of vasodilation to normal width may be obtained using phasedifferences between different wavelengths. The rate of change mayindicate vascular stiffness and a prediction of vascular health.

In another embodiment, the level of vasodilation may be used tocalibrate measurement of oxygen saturation SpO2 or other measurements ofconcentration of substances in blood flow. For example, measurements ofoxygen saturation levels may be in error during periods of vasodilation.These measurements of oxygen saturation during vasodilation may beidentified and flagged and/or the measurements may be calibrated inresponse to a level of vasodilation.

The biosensor described herein obtains PPG signals and measures arelative level of vasodilation of vessels and a period of vasodilation.For example, the PPG signal measures the pressure wave of blood flowthrough vessels. Vasodilation changes the propagation properties ofblood flow through vessels, and thus the PPG signal changes. The changesin PPG signals due to the changing propagation properties is reflectedin a transfer function generated from the PPG signals, e.g. timedifferences and wave shape differences between PPG signals. The transferfunction may be measured to determine a level of vasodilation in realtime.

FIG. 14 illustrates a schematic diagram of a graph 1400 of PPG signalsduring a period of vasodilation in vessels. At “rest”, a body respondsto caloric intake and vasodilation occurs normally as the body processesfood, insulin is dispensed, and arteries expand due to Nitric Oxide (NO)causing the outer muscle of the arteries to expand temporarily. Thisvasodilation is reflected in the PPG signal, and highly visible in thesignal to noise ratio.

The biosensor 100 obtained a PPG signal during vasodilation aftercaloric intake around a wavelength at 940 nm, a wavelength at 880 nm anda wavelength at 660 nm as shown in the PPG Signals for Wavelength Group1402 a. The biosensor 100 also obtained the spectral response for awavelength at 590 nm, a wavelength at 530 nm and a wavelength at 470 nmas shown in the PPG Signals for Wavelength Group 1402 b. The biosensor100 further obtained the spectral response for a wavelength at 405 nm, awavelength at 400 nm and a wavelength at 395 nm as shown in the PPGSignals for Wavelength Group 1402 c.

As shown in the graphs, the PPG signals reflect a period of vasodilation1408. The vasodilation 1304 a-c is reflected in the PPG signals during atime period between approximately 16.11.04 secs through approximately16.11.17 secs. In particular, a lower frequency component of the PPGsignals changes during the period of vasodilation 1408. This lowerfrequency component of the PPG signals includes the lower frequenciesnot affected by the pulsating blood flow (pressure wave) due to thecardiac cycle.

During vasodilation, the arteries and other vessels widen changing theabsorption properties of the vascular tissue. These changes inabsorption properties are due, e.g., by the increase in blood in thevascular tissue and the compression of surrounding tissue due to thewidening vessels. The PPG signals across wavelengths in the IR, visibleand UV spectrums are affected by the changing absorption properties ofthe vascular tissue due to vasodilation.

The level of vasodilation 1410 may be obtained from the PPG signals. Forexample, the change in low frequency from the PPG signal may becorrelated to a level of vasodilation. The level of vasodilation may beexpressed as a percentage change of the diameter or planar area of thevessel or percentage increase in blood flow during the period ofvasodilation. The level of vasodilation may alternatively be measured ina range such as 1-10, or in other manners.

The duration of the vasodilation may also be obtained from the PPGsignals. The beginning of vasodilation and end of vasodilation may beidentified from the PPG signals. For example, the vasodilation begins atapproximately 16.11.04 secs and ends at approximately 16.11.17 secs inGraph 1400 and so indicates a period of vasodilation of 13 seconds.

FIG. 15 illustrates a schematic diagram of a series of graphsillustrating the effects of vasodilation using the PPG signals shown inFIG. 14. The first graph 1502 illustrates R660/940 values that may beused to obtain a measurement of oxygen saturation SpO2. The vasodilationperiod 1512, seen at approximately 16.11.04 secs through approximately16.11.17 secs, affects the R values and thus the SpO2 measurements.Other measurements based on R values or relative amplitudes of PPGsignals are also affected by vasodilation. In an embodiment, an errorvalue or calibration may be determined for measurements of oxygensaturation SpO2 during a period of vasodilation. The error value orcalibration may depend on the level of vasodilation or change in Rvalues due to the vasodilation.

The second graph 1504 illustrates R values at R660/940, R405/940 andR395/940. The vasodilation period 1512, seen at approximately 16.11.04secs through approximately 16.11.17 secs, affects the R values,especially R values using PPG signals in the UV or near UV range. The Rvalues may be affected during the vasodilation period since the ratio ofthe amplitude of different wavelengths is used to obtain the R values.This may cause errors in the measurement of blood component levels. TheR values and/or measurements of blood component levels may becompensated due to the effect of vasodilation to correct errors duringperiods of vasodilation.

For example, during the expansion of vessels during a vasodilationperiod (e.g., due to NO or other EDRF), it may not be practical tomeasure the SpO2 amounts due to the error term present in the 940 & 660nm PPG signals. This effect of vasodilation is likely being observed bycurrent SpO2 meters. Errors in the measurement of SpO2 may be caused byundetected periods of vasodilation in current SpO2 meters. Vasodilationmay also cause errors in determinations of other blood components usingPPG signals. The measurement of the respiratory cycle in a PPG signal isalso affected during vasodilation.

The duration of the vasodilation effect may depend on the individual,the amount of the food ingested and the arterial rigidity. For example,the vessels of diabetic subjects are likely to expand less and have muchless change in amplitude of PPG signals during vasodilation due toinelasticity of the arteries due to arterial rigidity and endothelialdysfunction.

The graph 1506 illustrates the higher frequencies of the PPG signal at660 nm that may be used to measure the heart rate remains relativelyunaffected during the period of vasodilation.

The graph 1508 illustrates the lower frequencies of PPG signals at 940nm, 590 nm and 395 nm (e.g., the frequencies not affected by pulsatileblood flow). The characteristics of the lower frequencies of the PPGsignals change during the vasodilation period. The absorption propertiesof the vascular tissue vary due to changes in volume of blood. Inaddition, the widening of the vessels compresses the surrounding tissue.And the epidermis, the upper layer of the skin, may expand in responseto the widening vessels during vasodilation. The PPG signals are thusaffected by this change in absorption properties of the tissue, as seenin graph 1508.

The graph 1508 also illustrates that the PPG signals in differentspectrums exhibit a time or phase delay. For example, the PPG signal at940 nm in the IR range, the PPG signal at 590 nm in the visible range,and the PPG signal at 395 nm in the UV range have timing differences.This time delay is due in part to the different penetration depths ofthe wavelengths. Preferably, to determine this time delay, PPG signalsin an infrared range (IR) range from 650 nm to 1350 nm and PPG signalsoutside the IR range are compared to determine the time or phase delay.

The graph 1510 illustrates an elevated glucose level during thevasodilation period of about 140-152 mg/dl. At “rest”, a body respondsto caloric intake and vasodilation occurs normally as the body processesfood, insulin is dispensed, and arteries expand due to Nitric Oxide (NO)causing the outer muscle of the arteries to expand temporarily. Thiscaloric intake also elevates the glucose level temporarily. As shown inthe graphs, the SPO2 measurement is affected during the vasodilationperiod.

Vasodilation or vasoconstriction may also change the color or hue of theskin tissue due to expansion or contraction of the vessels. Thisincrease or decrease of blood flow may change the hue of the skin. Bymonitoring the hue of the skin, the biosensor 100 may detectvasodilation or other changes in blood circulation in the tissue. Forexample, a PPG signal in a visible light range such as at a yellow (590nm-560 nm) or Red (564 nm-580 nm) or Blue (490 nm-450 nm) wavelength maybe used to detect a change in hue of the skin.

Furthermore, the PPG signals in different spectrums exhibit phasedifferences or timing differences that correspond to the expansion andconstriction of the arteries during vasodilation or vasoconstriction.The phase differences are due in part to the different penetrationdepths of the wavelengths. At a same input power, light at higherwavelengths (IR light) penetrates vascular tissue deeper than light atlower wavelengths (UV light). The optical properties of the tissue areaffected by many factors, including but not limited to, skin-tone,tissue hydration, and tissue chemistry. In a sensor configuration wherethe light from the light source is backscattered to a sensor on the samesurface, the optical signal at the sensor includes a sum of all lightbackscattered that makes it to the focal surface after interacting withthe tissue. With the optical power being the same across allwavelengths, some of the light backscattered from the IR lightpenetrates deeper into the tissue than the UV light does. This meansthat the different wavelengths of light probe different depths oftissue.

When the heart beats, the arteries swell as fluid is pushed out of theheart. The leading edge of the swelling or pressure wave moves like a“bulge” through the arterial system. This system can be thought of as anelastically dampened hydraulic system. The pressure wave or bulge in thepulsatile blood flow moves from the lower tissue to the upper tissue.Thus, the deeper penetrating wavelengths (such as IR light) detect apressure wave first followed by the lesser penetrating wavelengths (suchas visible then UV light). The time delay in the “bulge” or pressurewave moving from the lower tissue into the upper tissue thus creates atime delay in a pressure waveform seen in the PPG signals at differentwavelengths. For example, as seen in FIG. 15, a waveform in the UV rangehas a time delay compared to a waveform in the IR range and a waveformin the visible range (390 nm to 700 nm). This time delay in thedifferent wavelengths is thus due to the depth of penetration into theskin of each wavelength.

Vasodilation changes the propagation of the pressure wave starting inthe deeper, larger arteries and then moving to the shallower, smallerones. In an embodiment described herein, this change in propagation ofthe pressure wave can be measured in the change in transfer functionfrom a wavelength that penetrates the tissue deeply (e.g. in the IRrange) to a wavelength that penetrates tissue much less deeply (e.g. inthe visible or UV range). This means that by measuring the change inshape and time delay of PPG signals of two or more wavelengths withdifferent penetration depths (e.g., wherein at least one is in thenear-IR window and one is not), information about vasodilation may bedetermined. Also, because the transfer function between the two depthsof penetration is affected by blood pressure, blood viscosity, tissueabsorption, and, in general, cardiovascular health, these otherparameters can be characterized as well. Features or parameters of thePPG signal that can be examined include, but are not limited to, thetime delay between the systolic points and diastolic points in differentwavelengths and the difference in dicrotic notch suppression betweenwavelengths.

FIG. 16 illustrates a schematic diagram 1600 illustrating phasedifferences and average low frequency levels during vasodilation usingthe PPG signals of various wavelengths from FIG. 14. The Graph 1602illustrates the average phase difference between a PPG signal at 940 nmand PPG signals of various wavelengths during the period ofvasodilation. The first time difference is 0 between 940 and itself. Thelast shown time difference is between 395 nm and 940 nm. The phasedifference or the timing difference between PPG signals in graph 1602illustrates a negative to positive timing which corresponds to theconstrictions and expansion of the arteries during vasodilation. Thephase delay between the PPG signals at different wavelengths is thusseen during a period of vasodilation.

The second graph 1604 illustrates the average “DC values” in PPG signalsof various wavelengths during the period of vasodilation. The “DCvalues” include DC components and/or low frequency components notgenerally affected by the pulsatile blood flow. The graph 1604illustrates that the average DC values I_(DC) are above a baselinenormal during the period of vasodilation. The average DC values increasedue to vasodilation, tissue characteristics of contracting or expandingmuscles and is proportional to the force applied to the muscle. So, theDC value (low frequencies not generally affected by the pulsatile bloodflow) can be used to determine a force applied during the movement.

Detection of Vascular Health

The endothelium lines the walls of vessels and helps to regulatevascular function. In the vasculature, insulin is released in responseto ingestion or hunger. The insulin activates two distinct signalingpath-ways in the endothelium that result in secretion of nitric oxide(NO) and endothelin (ET-1), respectively.

FIG. 17A illustrates a schematic block diagram of an arterial wall underhealthy conditions 1702. Smooth muscle cells respond to NO as avasodilator and endothelin (ET-1) as a vasoconstrictor. ET-1 incitesconstriction in the smooth muscle cells by binding to ET_(A) and ET_(B)receptors. In the vasculature, the ET_(A) receptor is mainly located onvascular smooth muscle cells and mediates vasoconstriction. The ET_(B)receptor is primarily located on endothelial cells but may also bepresent on vascular smooth muscle cells. Stimulation of the endothelialET_(B) receptor results in release of NO and prostacyclin which causesvasodilatation, whereas stimulation of the vascular smooth muscle cellET_(B) receptor results in vasoconstriction. Thus, the net effectproduced by ET-1 is determined on the receptor localization and thebalance between ET_(A) and ET_(B) receptors.

Endothelial cells also mediate rapid responses to neural signals forblood vessel dilation, by releasing NO to make smooth muscles relax inthe vessel wall. Production of NO counteracts or mediates theconstricting effects of ET-1 in response to insulin in vasculatures.Insulin stimulates NO production in endothelial cells by subsequentlyactivating the intracellular enzymes 1-phosphatidylinositol 3-kinase(PI3-ki-nase) and Akt, which activates endothelial NO synthase. NO,stimulated by higher insulin doses, is thought to be the underlyingagent in insulin-mediated, endothelium-dependent vasodilation. Inhealthy arteries, smaller levels of ET-1 are produced in comparison toNO levels, and so the bioavailability of NO is preserved.

FIG. 17B is a schematic block diagram of an arterial wall with vasculardysfunction. In vascular dysfunction, there is an increased expressionof ET-1 in smooth muscle cells and macrophages. There is also anincreased expression of ET_(B) receptors on smooth muscle cellsmediating vasoconstriction. In addition, ET-1 may decrease endothelialNO synthase (eNOS) expression, thereby reducing NO production. Both theET_(A) and the ET_(B) receptors on smooth muscle cells may mediateformation of superoxide (O₂) in endothelial dysfunction. Superoxide willdecrease the biological activity of NO by forming peroxynitrate (ONOO—).This increases the effect of ET-1 and decreases the effect of NO onsmooth muscle cells. Clinical evidence in obesity and diabetes suggestEndothelial dysfunction as a failure to vasodilate adequately afterapplication of an endothelium-dependent vasodilator but also excessvasoconstrictor tone. Thus, ET-1 contributes to endothelial dysfunctionboth directly, through its vasoconstrictor effects, and indirectly,through inhibitory effects on NO production.

Collectively, the balance of these effects in endothelial dysfunction isshifted towards more vasoconstriction, inflammation and oxidativestress. This pathogenic role of the altered expression and biologicalactions of ET-1 in vascular dysfunction may lead to the development ofcardiovascular disease, atherosclerosis and hypertension. For example,dysfunction of the vascular endothelium is an early finding in thedevelopment of cardiovascular disease and is closely related to clinicalevents in patients with atherosclerosis and hypertension.

Determination of ET-1 and NO Balance

As discussed above, in the vascular system, insulin stimulates both ET-1and NO activity. An imbalance between the efficacy of these substancesmay be involved in the pathophysiology of heart disease, hypertensionand atherosclerosis. Thus, a device and method to determine the balanceof these substances in vivo would be important in determininginsulin-resistance and vascular health.

FIG. 18 illustrates a schematic diagram of PPG signals obtained duringperiods of insulin release events in vessels. At “rest”, a body respondsto caloric intake by releasing insulin into the blood stream. Thisinsulin release stimulates ET-1 and NO activity.

In the example of Graph 1800, the biosensor 100 obtained PPG signalsover a seven minute period between 28 mins and 35 mins around aplurality of wavelengths at 940 nm, 630 nm, 590 nm, 530 nm, 465 nm and395 nm. The PPG signals reflect “pulses” in response to discrete releaseof insulin in the bloodstream. The PPG signals reflect the insulinrelease events at a first pulse 1804 a around 29.15 mins, a second pulse1804 b around 30.35 mins, a third pulse 1804 c around 32.10 mins, and afourth pulse 1804 d around 33 mins. Vascular strain occurs duringrelease of localized insulin (insulin release events) as part of theglucose regulation processes. This vascular strain impairs the PPGsignals temporarily during the interaction of the ET-1 and NO agentsreleased during the insulin release events.

Graph 1802 illustrates the PPG signals due to pulsatile blood flowI_(AC). The I_(DC) signal has been filtered from the PPG signals in thisexample. The I_(AC) signal reflects the ET-1 and NO response in thevessels due to the insulin release events at a first pulse around 29.15mins, a second pulse around 30.35 mins, a third pulse around 32.10 mins,and a fourth pulse around 33 mins. The smooth muscle cells of arterialwalls tighten during chemical reactions of each insulin pulse. Thistemporary stiffing of the arterial structure causes a dampening effecton the PPG signals during the insulin release event. The 630 nm & 940 nmoptical wavelengths are probing at deeper arterial/venous tissuestructures wherein the smooth muscle walls are thicker and exhibit ahigher stiffness factor under chemical induced strain such as an insulinrelease. The blood flow of the outer tissues (microvacuoles) includeless smooth muscle tissue thickness and therefore respond with a morepronounced PPG signal pulse at 395 nm, 465 nm, 530 nm and 590 nm. Thus,the PPG pulses at these wavelengths are less pronounced.

Due to the higher level of insulin release, the ET-1 and NO response atthe first pulse 1806 a and the second pulse 1806 b have a greaterconstricting effect on the vessels. The vasoconstriction decreases inthe third and fourth pulses due to the decrease in insulin release atET-1 and NO responses 1806 c and 1806 d. In addition, the NO levels mayalso have accumulated to further mediate the effects of ET-1. Thus, thevasoconstriction is lessened in response to the later insulin releaseevents 1804 c and 1804 d.

The vasoconstriction in response to insulin release is thus affected bythe balance of ET-1 and NO as well as vascular disease such asatherosclerosis. By measuring the relative vasoconstriction or change inarterial diameter in response to insulin release, vascular health may beassessed using the biosensor 100.

FIG. 19 illustrates a schematic diagram of graphs comparing phase delayand pulse shape correlation in a plurality of PPG signals during insulinrelease in vivo. As shown in Graph 1800, in the example of Graph 1900,the biosensor 100 obtained PPG signals over a seven minute periodbetween 28 mins and 35 mins around a plurality of wavelengths at 940 nm,630 nm, 590 nm, 530 nm, 465 nm and 395 nm. The PPG signals reflect“pulses” in response to discrete release of insulin in the bloodstream.Graph 1902 illustrates the PPG signals due to pulsatile blood flowI_(AC) with low frequency signals I_(DC) filtered therefrom.

In Graph 1904 and 1906, the R value 1908 of 395 nm/530 nm isillustrated. In addition, a correlation is computed between the PPGwaveform at 940 nm and the PPG waveform at 395 nm to obtain a PulseShape Correlation 1910 and a Phase Delay 1912. The PPG signals areprocessed using, e.g., a cross correlation function or a Hilberttransformation or another algorithm that determines similarities inpulse shape and temporal relationship between PPG signals. For example,the time delay between the two signals can also be calculated at eachtime instant from the phase shift of their wavelet transforms.

The Pulse Shape Correlation 1910 and Phase Delay 1912 include effects ofouter and inner tissue layers of vessels on the PPG signal. When themuscle cells constrict during vasoconstriction, the optical propertiesare altered. In addition, the change in NO level affects the PPG signalaround 395 nm.

In healthy persons, arterial walls are more flexible and thus have agreater relative change in diameter in response to insulin. The PulseShape Correlation 1910 and Phase Delay 1912 signals reflect a greaterchange in signal levels in response to insulin. The R value pulses arecorrespondingly more pronounced. The phase timing is inverselyproportional to the arterial diameters.

In patients having endothelium dysfunction, the arteries exhibitstiffness with a decreased relative change in diameter. Endotheliumdysfunction may be found in patients with diseases such asatherosclerosis, hypertension and diabetes. The Pulse Shape Correlation1910 and Phase Delay 1912 respond with a decreased relative amplitudechange during an insulin release event. The Pulse Shape Correlation 1910and Phase Delay 1912 may thus be used to determine vascular health.

In an embodiment, the phase delay 1912, pulse shape correlation 1910 andR value 1908 may be used to determine whether ET-1 or NO is moredominant in response to insulin. For example, the average or mean rangeof one or more of these measurements in a healthy population ismeasured. Then, an individual measurement is compared to the average ormean range of one or more of phase delay 1912, pulse shape correlation1910 and R value 1908. The comparison may be used to obtain whether animbalance is present between the effects of ET-1 and NO. An imbalance inthe effects of the two substances has an increased vasoconstrictoreffect on vessels due to an increase in ET-1 activity.

In addition, this change in propagation of the pressure wave can bemeasured in the change in transfer function from a wavelength thatpenetrates the tissue deeply (e.g. in the IR range) to a wavelength thatpenetrates tissue much less deeply (e.g. in the visible or UV range).This means that by measuring the change in pulse shape and phase delayof the PPG signals at two or more wavelengths with different penetrationdepths (e.g., wherein at least one is in the near-IR window and one isnot), information about vasoconstriction may be determined.

Biosensor Detection of Vascular Health

FIG. 20 illustrates a schematic block diagram of an insulin response ofa young healthy male and a middle-aged male. The Graph 2000 illustratesan R value 2004 of 395 nm/530 nm during an insulin response in amiddle-aged male patient obtained using the biosensor 100. In the ET-1and NO response 2010, the R values 2004 shows a subdued response due toan increased arterial stiffness and/or ET-1 prominence. The ET-1/NOresponse 2010 is more typical of vasoconstriction.

The Graph 2002 illustrates an R value 2006 of 395 nm/530 nm during aninsulin response in a young male patient. In the ET-1 and NO response2012, the R values 2006 have a relatively greater range due to a healthyvascular system. The ET-1/NO response 2012 is more typical ofvasodilation. Thus, by comparing the R value data of healthy persons ina general population with an individual's measurement, the presence ofan increased arterial stiffness and/or ET-1 prominence may bedetermined.

FIG. 21 illustrates a schematic diagram of graphs comparing phase offsetand pulse shape waveform in a plurality of PPG signals during insulinrelease in an adolescent male. In the example of Graph 2100, thebiosensor 100 obtained PPG signals over an approximately three minuteperiod around a plurality of wavelengths at 940 nm, 630 nm, 590 nm, 530nm, 465 nm and 395 nm. The PPG signals reflect a discrete insulinrelease event 2108 in the bloodstream. The insulin release 2108 includesa marked PPG pulse in a first wavelength having a high absorptioncoefficient for NO, (e.g. 395 nm) wherein the amplitude of the pulse isat least greater than twice expected from a heart rate pulse.

Graph 2102 illustrates the PPG signals due to pulsatile blood flowI_(AC). The I_(AC) signal reflects an ET-1 and NO response 2110. TheI_(AC) signal has at least a 50% decrease in amplitude during theinsulin release event 2108. One reason for the decrease in amplitudeincludes the constriction of the smooth muscle cells in vessels thataffects the absorption properties of the tissue.

In Graph 2104 and 2106, the R value 2112 of 395 nm/530 nm isillustrated. In addition, a correlation between the PPG waveform at 940nm and the PPG waveform at 395 nm is also illustrated. The correlationincludes a Phase Delay 2114 and a Pulse Shape Correlation 2116. The PPGsignals are processed using a cross correlation function or a Hilberttransformation or another algorithm that determines similarities inpulse shape and temporal relationship between the PPG signals. Forexample, the time delay between the two signals can also be calculatedat each time instant from the phase shift of their wavelet transforms.

The Phase Delay 2114 and a Pulse Shape Correlation 2116 includes effectsof outer and inner tissue layers of vessels on the PPG signal, e.g.muscle cells during vasoconstriction. The Phase Delay 2114 and a PulseShape Correlation 2116 may be mapped to a vessel diameter or level ofvasoconstriction/vasodilation.

FIG. 22 illustrates a schematic diagram of an insulin response in theadolescent male in greater detail. Graph 2200 illustrates the PPGsignals due to pulsatile blood flow I_(AC) during the period of insulinrelease 2108. The ET-1 and NO response 2110 includes a decrease inamplitude of the I_(AC) signal due to vasoconstriction. The constrictingshallow muscle cells affect the optical properties of the PPG signalsduring this interval.

The period of vasoconstriction 2204 may be determined based on theamplitude changes of the PPG signals. At the beginning of the period ofvasoconstriction 2204, the amplitudes of the I_(AC) signal begin todecrease and then to slowly increase until the amplitudes of the I_(AC)signal return to average at the end of the period of vasoconstriction2204. The period of vasoconstriction 2304 is approximately between 21sec and 31 sec. in this example.

A level of vasoconstriction 2202 may be determined, e.g., from anaverage peak to peak amplitude of the PPG signals prior to or after theperiod of vasoconstriction and the lowest peak to peak amplitude of thePPG signals during the period of vasoconstriction. The level ofvasoconstriction may be measured in other manners, such as average peakvalue to lowest peak value during the period of vasoconstriction.

FIG. 23 illustrates a schematic diagram of an insulin response in theadolescent. Graph 2300 illustrates the PPG signals during the insulinrelease event 2108 in greater detail. The Graph 2300 illustrates thatthe insulin release generates a constricting response in the vesselsover an approximately 10 second interval during the period ofvasoconstriction 2204.

FIG. 24 illustrates a schematic diagram of graphs comparing phase offsetand pulse shape waveform in a plurality of PPG signals during an insulinrelease event in a middle-aged male. In the example of Graph 2400, thebiosensor 100 obtained PPG signals over a 2:49 minute period around aplurality of wavelengths at 940 nm, 630 nm, 590 nm, 530 nm, 465 nm and395 nm. The PPG signals reflect a pulse in response to a discreteinsulin release 2408 in the bloodstream. The insulin release 2108includes a marked PPG pulse in a first wavelength having a highabsorption coefficient for NO, wherein the amplitude of the pulse is atleast greater than twice expected from a heart rate pulse.

Graph 2402 illustrates the PPG signals due to pulsatile blood flowI_(AC). The I_(AC) signal reflects an ET-1 and NO response 2410 in thevessels due to the insulin release 2408. The I_(AC) signal has at leasta 50% decrease in amplitude during the insulin release event 2408.

In Graph 2404 and 2406, the R value 2412 of 395 nm/530 nm isillustrated. In addition, a correlation between the PPG waveform at 940nm and the PPG waveform at 395 nm is illustrated as Phase Delay 2414 andPulse Shape Correlation 2416. The PPG signals are processed using across correlation function or a Hilbert transformation or anotheralgorithm that determine similarities in pulse shape and temporalrelationship between PPG signals. For example, the time delay betweenthe two signals can also be calculated at each time instant from thephase shift of their wavelet transforms.

The Phase Delay 2414 and a Pulse Shape Correlation 2416 includes effectsof outer and inner tissue layers of vessels on the PPG signal, e.g.muscle cells during vasoconstriction. The Phase Delay 2414 and a PulseShape Correlation 2416 may be mapped to a vessel diameter or level ofvasoconstriction/vasodilation.

FIG. 25 illustrates a schematic diagram of an insulin response in amiddle aged male in greater detail. From FIG. 24, graph 2402 illustratesthe PPG signals due to pulsatile blood flow I_(AC) during the period ofinsulin release 2408. The Graph 2500 illustrates that the ET-1 and NOresponse 2410 from graph 2402 in greater detail. Graph 2500 reflects thedecrease in amplitude of the I_(AC) signal due to vasoconstriction. Whensmooth muscles cells tighten causing vasoconstriction, the I_(AC) signalamplitude decreases in magnitude. The constricting shallow muscle cellsaffect the optical properties of the PPG signals during this interval.

The period of vasoconstriction in this example is from about 20 secondsto at least 28 seconds, e.g. the period of vasoconstriction 2504. Alevel of vasoconstriction 2502 may be determined, e.g., from an averagepeak to peak amplitude of the PPG signals prior to or after the periodof vasoconstriction and the lowest peak to peak amplitude of the PPGsignals during the period of vasoconstriction. The level ofvasoconstriction may be measured in other manners, such as average peakvalue to lowest peak value during the period of vasoconstriction.

FIG. 26 illustrates a schematic diagram of an insulin response in amiddle aged male in greater detail. Graph 2600 illustrates the PPGsignals during the period of insulin release 2408. The Graph 2600illustrates that the insulin release generates a constricting responsein the vessels during the period of vasoconstriction. The constrictingshallow muscle cells affect the optical properties of the PPG signalsduring this interval.

Comparing the PPG signals detected during the insulin release betweenthe adolescent male and the middle aged male, the PPG signals indicatethat the vasoconstriction is relatively less in the middle aged male.The decrease in vasoconstriction is expected due to age related arterialstiffness and arteriosclerosis. This age-related difference invasoconstriction can be due to decreased elastic production fromfibrinogen, associated with ageing, or hypertension or pathologicalconditions such as atherosclerosis. The smooth muscle cells of theadolescent male may also be stronger, and the elastic lamina thatsurrounds the lumen of the artery may be more resilient and flexible atthat age. This demonstrates that a level of vasoconstriction may bedetermined from the PPG signals and compared to healthy values (such asin the adolescent male) to determine vascular health.

FIG. 27 illustrates a schematic flow diagram of an embodiment of amethod 2700 for determining vascular health using the biosensor 100. Thebiosensor 100 detects PPG signals at a plurality of wavelengthsreflected from skin tissue at 2702. Preferably, the first wavelength hasa high absorption coefficient for NO and is approximately 395 nm or in arange from 380 to 410 and a lower depth of penetration into the tissue.The second wavelength has a lower absorption coefficient for NO and isapproximately in a range from 510 nm to 550 nm or is in an IR range suchas 940 nm and has a greater depth of penetration into the tissue.

The PPG signals are measured over a period of time that preferablyincludes one or more insulin release events, such as after ingestion,wherein insulin is released into the blood stream. The insulin releaseis reflected by a marked PPG pulse in the first wavelength having a highabsorption coefficient for NO. The pulse has a 5-10 second duration,wherein the amplitude of the pulse is at least greater than twiceexpected from a heart rate pulse. The pulses due to insulin release alsohave a much lower frequency than a heart rate. The insulin release eventmay thus be identified in the PPG signals using one or more of thesecharacteristics.

One or more parameters derived using the PPG signals during the insulinrelease event is determined and compared at 2704. For example, a crosscorrelation function may determine a phase offset between the PPGsignals and/or pulse shape correlations during the insulin releaseevent. The PPG signals may also be processed using other crosscorrelation functions or a Hilbert transformation or another algorithmthat determine similarities in pulse shape and temporal relationshipbetween PPG signals.

A measurement of vascular health is obtained using the one or moreparameters at 2706. For example, a measurement of vasoconstriction orvasodilation may be obtained, such as a vessel diameter or percentage ofchange in diameter. The relative efficacy of ET-1 and NO may beestimated based on the measurement of diameter change and level ofinsulin release. A level of arterial stiffness may be determined usingthe measurement of the diameter change and level of insulin release andcomparing to such measurements in a general sampling of healthy personswithout vascular dysfunction.

FIG. 28 illustrates a schematic flow diagram of an embodiment of amethod 2800 for determining an efficacy balance of ET-1 and NO in smoothmuscle cells of vessels. The vasoconstriction or vasodilation inresponse to insulin release is affected by the balance of ET-1 and NO aswell as vascular disease such as atherosclerosis. By measuring therelative vasoconstriction or change in arterial diameter in response toinsulin release, the relative efficacy and balance of ET-1 and NO may beassessed using the biosensor 100.

The phase offset and/or correlation of pulse shape of two or more PPGsignals is determined over the period of time including the insulinrelease at 2802. For example, the first wavelength has a high absorptioncoefficient for NO and a lower penetration depth into tissue, and thesecond wavelength has a lower absorption coefficient for NO and a higherpenetration depth into tissue. A cross correlation function may be usedto determine the phase offset and/or pulse shape correlations or aHilbert transformation or another algorithm that determine similaritiesin pulse shape and temporal relationship between PPG signals.

An imbalance in the effects of the two substances has an increasedvasoconstrictor effect on vessels due to an increase in ET-1 activityand suppression of NO efficacy. The change in diameter of vessels duringinsulin release may be determined at 2804 and compared to a healthyindividual of similar age with no vascular dysfunction at 2804.Increased relative levels of vasoconstriction may be indicative ofincreased ET-1 activity due to an imbalance of ET-1 and NO efficacycaused by insulin-resistance disease such as diabetes.

The phase delay may also provide an indication of the balance of ET-1and NO in response to insulin. For example, the R value is compared tosystolic peaks of the phase delay to determine a relative level ofvasoconstriction or change in diameter of vessels. The phase offsetbetween two or more of the PPG signals in different spectrums, or havingdifferent depths of penetration of tissue, is measured. The phase offsetmay be used to determine presence of vasodilation/vasoconstriction inthe tissue. For example, in normal tissue, the PPG signals exhibit onlya slight difference in phase or timing when nominal vasodilation isoccurring in the tissue. When the PPG signals have a greater differencein phase or timing, this indicates that blood flow in the tissue nearthe surface is decreased, e.g. due to vasoconstriction, due to low bloodcirculation level or an imbalance of NO and ET-1 or arterial stiffness.When blood flow is increased to the tissue, the PPG signals at UV and IRwavelengths exhibit a lower variance in pulse shape and a highercorrelation value. This decrease in the difference in the pulse shape ofthe PPG signals at the different wavelengths indicates an increase ofblood flow, e.g. due to vasodilation.

The phase offset and pulse shape correlation may be mapped to a level ofvasodilation/vasoconstriction, e.g. using a calibration table orfunction. The level of vasodilation/vasoconstriction and a period ofvasodilation/vasoconstriction may thus be determined using the phasedifferences and pulse shape correlations between the PPG signals at thedifferent wavelengths. The above described parameters of the PPG signalsmay also be used to determine a period of vasoconstriction using similarmethods.

In another aspect, R values are determined using the PPG signals atleast two wavelengths, such as R_(660 nm/940 nm) or R_(405/940) orR_(395 nm/940 nm). The level of vasodilation or period of vasodilationmay be determined using changes in amplitude of one or more R values.

The level of vasoconstriction/vasodilation may be compared to an insulinlevel to determine the balance of the effects of ET-1 and NO at 2808.For example, the level of vasoconstriction/vasodilation for a knowninsulin level or during an average insulin release event may bedetermined in individuals with healthy vascular function. A calibrationtable or function may store a mapping of a range ofvasoconstriction/vasodilation and/or an average period ofvasoconstriction/vasodilation for one or more levels of insulin releaseby testing a general population of healthy individuals. The level ofvasodilation may be represented as a measurement of one or more of: apercentage of change in arterial width, diameter or planar area or achange in blood flow or volume, etc. These comparisons may thus indicatea balance of efficacy between ET-1 and NO at 2808.

In addition, arterial stiffness may decrease a relative level ofvasodilation compared to an average or normal range. The rate of changeof the width of the artery at a beginning or end of vasodilation may beused as an indicator of arterial stiffness. A reduction in elasticity ofarteries may decrease the rate of change in the width of the artery andthus the rate of change in the level of vasodilation. These comparisonsof the rate of change of the width of vessels may also be used toindicate a measurement of arterial stiffness. These determinations mayalso factor in the determination of whether the cause of reducedvasoconstriction/vasodilation is due to an imbalance of ET-1 and NO ordue to arterial stiffness during an insulin release event.

Measurement of Insulin Levels

FIG. 29 illustrates a schematic flow diagram of an embodiment of amethod 2900 for determining an insulin level in blood flow. Thebiosensor 100 monitors PPG signals at a plurality of wavelengthsreflected from skin tissue over a period of time, such as 5 minutes to24 hours. Preferably, a first wavelength has a high absorptioncoefficient for NO and is approximately 395 nm or in a range from 380 to410. A second wavelength has a lower absorption coefficient for NO andis approximately 530 nm or in a range from 510 nm to 550 nm or is in anIR range such as 940 nm.

The PPG signals are analyzed to identify one or more insulin releaseevents at 2902. For example, after ingestion, insulin is naturallyreleased into the blood stream. The insulin release effects a marked PPGpulse in the first wavelength having a high absorption coefficient forNO. The PPG pulse, e.g., has a longer duration than a PPG pulse of aheart rate. For example, the PPG pulse during an insulin release eventhas an approximately 5-10 second duration, wherein the change inamplitude of the PPG pulse is at least greater than twice expected froma heart rate pulse. Signal analysis using pattern recognition may beemployed with the PPG signals to identify the insulin pulse.

An R value curve is obtained over the period of the insulin releaseevent, using PPG signals having the first and second wavelength, such asan R value of 395 nm/530 nm or 395 nm/940 nm at 2904. The R value curveduring the insulin release event is analyzed to determine an insulinlevel at 2906. For example, an area under the R value curve isdetermined during the insulin release event. A calibration table orcurve is tabulated that associates the area to the insulin level. Thecalibration may be performed on an individual using a blood test todetermine insulin levels during a calibration phase of the biosensor.Alternatively, the calibration may be predetermined from testing of ageneral population. Though an area under the R value curve is describedfor the calibration, other parameters obtained from the pulse of the PPGsignals during the insulin release event may be used to determineinsulin levels, such as an average R value or I_(AC) value.

Insulin is usually secreted in discrete amounts one or more timesdepending on the stage of digestion. Thus, multiple insulin releaseevents may be detected within a short time period after ingestion. Theinsulin level may be determined for additional insulin release eventsusing the R value curve and calibration table at 2908. The cumulativeinsulin released over a time period may then be determined at 2910 bysummation of the individual insulin release events during the timeperiod.

The stage of digestion may also be determined using identification ofthe insulin release events from the PPG signals. For example, theinsulin release events are more frequent after ingestion during stage 1and stage 2 of digestion and are less frequent when hungry.Correspondingly, the frequency of PPG pulses due to insulin releaseevents increases in response to different stages of digestion. Incontrast, the frequency of the PPG pulses due to insulin release eventsdecreases in response to fasting or hunger. Thus, by measuring thefrequency or time between insulin release events using the PPG signals,a stage of digestion may be identified or a level of fasting or hungermay be identified at 2912.

Measurement of Glucose Levels

As described herein, the biosensor may determine a glucose level byaveraging an R value over a short period of time (e.g., around 2-3minutes) and using a calibration to obtain a glucose level associatedwith the R value. This method has predictable results for healthypersons with little to no vascular dysfunction. However, for personswith certain diseases, e.g. affecting arterial health, this method maynot provide accurate results due to unhealthy vasoconstriction ofarterioles near the surface of the skin or tissue. For example, diabetescreates extreme vasoconstriction that affects the R value and results ininaccurate correlations to NO and glucose levels.

FIG. 30 illustrates schematic diagrams of measurements of glucose levelsin a plurality of patients using the biosensor in a clinical trial. Inthis example, the patients ingested a caloric intake, and then areference glucose was tested at discrete points using a blood test. Inaddition, the biosensor 100 detected an R value at 395 nm/940 nm at thediscrete points. The patients in graphs 3000, 3002 and 3004 had aseemingly healthy vascular function and NO response. The R valueapproximately tracked the trend in the reference glucose. Thus, the Rvalue provides a predictable tracking of trends in glucose, and auniversal calibration table or curve may be compiled to correlate Rvalues and glucose levels in these patients.

However, the patients in graphs 3006 and 3008 exhibited vasculardysfunction. The R value diverged from the reference glucose at one ormore of the discrete points. For example, the vasodilation effect duringphase 2 of digestion created unexpected results in the R values. Thus,in patients with atypical vascular responses, individual calibration ofglucose levels to R values may need to be performed.

FIG. 31 illustrates schematic diagrams of measurements of glucose levelsin a plurality of patients using the biosensor 100 in a clinical trial.In this example, the reference glucose is displayed with a predictedglucose value that is obtained using the R_(395 nm/940 nm) values shownin FIG. 30. The R values for patients in graphs 3000, 3002 and 3004 witha seemingly healthy vascular function and NO response were correlated tothe predicted glucose values using a universal calibration. Theuniversal calibration correlates R values and glucose values based on aclinical testing from a general sample population of persons withhealthy vascular systems. The universal calibration may include a table,equation, factor or curve. Thus, the R value provides a predictabletracking of trends in glucose for patients with a relatively healthyvascular response, and a universal calibration may be compiled tocorrelate R values and glucose levels in these patients.

However, the R values for patients in graphs 3106 and 3108 arecorrelated to the predicted glucose values using individualcalibrations. For example, the R value is obtained, and an interimglucose value is estimated using the universal calibration. The interimglucose value is then adjusted using an individual calibration. Adifference or other correlation between the interim glucose value andthe reference glucose is determined at one or more points of time. Thedifference or other correlation is used as an individual calibration toadjust the interim glucose value to the predicted glucose levels shownin Graphs 3106 and 3108. Thus, for patients with vascular dysfunction,an individual calibration is used to obtain the predicted glucose levelsfrom the R values

In another embodiment, the individual calibration directly correlatesthe R values to the predicted glucose level for patients with vasculardysfunction. The reference glucose at one or more discrete points iscompared to the R_(395nm/940 nm) values at the same discrete points, andthe individual calibration is obtained.

The individual calibration should be recalculated at least every 2-3months due to potential change in vascular function. For example,arteriolosclerosis or insulin resistance may further deteriorate thevascular health such that the vessels exhibit increasedvasoconstriction. This deterioration may affect the level ofvasoconstriction in vessels and the correlation between R values andglucose levels.

FIG. 32 illustrates a schematic flow diagram of an embodiment of amethod 3200 for determining glucose levels of a patient with atypicalvascular function. The biosensor 100 determines that a user has vasculardysfunction or a disease that typically leads to vascular dysfunction,such as diabetes, heart disease or arteriolosclerosis, at 3202. The usermay input or request individual calibration. The PPG signals areobtained, preferably at a first wavelength with a high absorptioncoefficient for NO, such as 395 nm or in a range around 380 nm to 410 nmand determining a measurement value using the PPG signals at 3204. Themeasurement value may include, e.g., an R value at 395/940 or 395/530wavelength ratios.

The biosensor 100 may then determine a level of glucose using themeasurement value and an individual calibration at 3206. For example,the R value is obtained, and an interim glucose value is estimated usingthe universal calibration. The interim glucose value is then adjustedusing an individual calibration. In another embodiment, the individualcalibration directly correlates the R values to the predicted glucoselevel for patients with vascular dysfunction. Thus, for patients withvascular dysfunction, an individual calibration is used to obtain thepredicted glucose levels from the R values.

The individual calibration should be re-evaluated periodically at 3208.For example, the individual calibration should be updated at least every2-3 months due to potential changes in vascular function.

FIG. 33 illustrates a schematic flow diagram of another embodiment of amethod 3300 for determining glucose levels of a patient with atypicalvascular function. As shown in the example of Graph 1900, the biosensor100 obtains PPG signals over a time period between around a plurality ofwavelengths at 940 nm, 630 nm, 590 nm, 530 nm, 440 nm and 395 nm. The“pulses” in response to discrete release of insulin in the bloodstreamare identified in the PPG signals. Then a correlation is computedbetween the PPG waveform with a low absorption coefficient for NO (e.g.,440 nm, 530 nm or another wavelength in the visible range or in the IRrange) and the PPG waveform with a high absorption coefficient for NO(e.g., at 395 nm or in a range of +/−10 nm of 395 nm) during the periodof release of insulin to obtain a Pulse Shape Correlation and a PhaseDelay at 3302. The PPG signals are processed using, e.g., a crosscorrelation function or a Hilbert transformation or another algorithmthat determines similarities in pulse shape and temporal relationshipbetween the PPG signals.

The phase offset or waveform correlation may then be used to determine afactor to “normalize” an R value to obtain a normalized R value at 3306.Thus, the normalization factor may account for increasedvasoconstriction due to vascular dysfunction. For example, the R valuemay be divided by an averaged phase offset factor or an averaged pulseshape correlation to determine the “normalized” R value. The normalizedR value is then correlated to a glucose level using a universalcalibration table or curve at 3308. The normalization factor compensatesthe R value in patients with vascular dysfunction.

In another embodiment, a plurality of calibrations may be implemented,each assigned to one or more different normalization factors. Theglucose level is determined using the calibration table associated withthe determined normalization factor.

Identification of Deep Inhalation

FIG. 34 illustrates a schematic diagram of graphs of PPG signals duringdeep inhalation. A rapid, deep inspiration is also known to inducevasoconstriction of skin arterioles. In particular, a deep inhalationmay vastly reduce the amplitude of PPG pulse waveforms and alsointroduce marked low-frequency components as a consequence ofvasoconstriction and subsequent vasodilatation. These changes due todeep inspiration may create difficulties in accurately identifying PPGwaveform features, such as insulin release periods. This also increasesthe error when computing physiological measures.

Graph 3400 illustrates PPG signal obtained during a deep inhalation 3402around a plurality of wavelengths at 940 nm, 630 nm, 590 nm, 530 nm, 465nm and 395 nm. The deep inhalation caused a decrease in the PPG pulseamplitude along with a characteristic low-frequency trend as seen inGraph 3404. Graph 3404 shows the I_(AC) signal due to pulsatile bloodflow. Because of the excessively low amplitude indicative ofvasoconstriction 3406, the deep inhalation may be mistaken for aninsulin release event.

Graph 3408 illustrates the R value 3410 of 395 nm/530 nm is illustrated.In addition, a correlation is computed between the PPG waveform at 940nm and the PPG waveform at 395 nm to obtain a Phase Delay 3412. The PPGsignals are processed using, e.g., a cross correlation function or aHilbert transformation or another algorithm that determines similaritiesin pulse shape and temporal relationship between PPG signals. Forexample, the time delay between the two signals can also be calculatedat each time instant from the phase shift of their wavelet transforms.

The R value 3410 has a low amplitude indicative of vasoconstriction3406, such as in insulin release or deep inhalation. However, the phasedelay 3412 does not indicate an insulin release. As seen in Graph 2404in FIG. 24, the phase delay 2414 in response to an insulin release has acorresponding pulse with a large amplitude change. The phase delay 3412in response to the deep inhalation 3402 fails to include such a pulse ata time corresponding to the vasoconstriction 3406. Thus, thevasoconstriction 3406 may be identified as an inhalation or othervasoconstriction causing event and not due to insulin release. Suchpattern recognition may be performed to identify insulin release eventsrecorded by the PPG signals.

Detection of Sepsis

FIG. 35 illustrates a schematic diagram of graphs of PPG signalsdetected from a critical care patient diagnosed with sepsis. Thebiosensor 100 obtained PPG signals over a time period of approximatelytwo minutes around a plurality of wavelengths at 940 nm, 630 nm, 590 nm,530 nm, 440 nm and 390 nm. Graph 3500 illustrates the I_(DC) signal 3508of low frequency signals with the I_(AC) signal filtered. The I_(AC)signal has erratic frequency pulses with high amplitude peaks,especially at the 390 nm with a high absorption coefficient for NO.Graph 3502 illustrates the R value 3510 obtained for 390 nm/940 nm. TheR value 3510 also has an erratic signal that fluctuates between positiveand negative values with extremely high amplitude peaks. The R value inthis example exceeds 200.

These large peaks in sepsis patients may initially create difficultiesin accurately identifying PPG waveform features, such as insulin releaseperiods. However, in patients with sepsis, the PPG responses are erraticin frequency with peaks exceeding amplitudes typically seen in insulinrelease periods. In addition, the R values for 390 nm/940 nm hasabnormally high values exceeding 10 times normal and then may also havenegative values.

Detection of Digestion or Hunger

FIG. 36 illustrates a schematic diagram of graphs of PPG signals duringperiods of ingestion and fasting. Graph 3600 illustrates an R valueobtained from PPG signals at 395 nm and 940 nm over an approximate 88minute time period. The patient ingested food at approximately 19minutes. The Graph 3600 shows insulin release pulses with a frequency ofapproximately every 2-3 minutes after ingestion. In contrast, Graph 3602shows PPG signal response over an approximately 102 minute period. Thepatient has not ingested caloric intake. The insulin release pulses havea frequency of approximately every 10-20 minutes. Thus, by determining afrequency or average period between insulin release pulses, an ingestiontime or digestion stage may be determined. In addition, a hunger levelor time from ingestion may also be determined from the time betweeninsulin release pulses.

The stage of digestion may thus be determined using identification ofthe insulin release events from the PPG signals. For example, theinsulin release events are more frequent after ingestion during stage 1and stage 2 of digestion and are less frequent in response to fasting orhunger. Thus, by measuring the frequency or time between insulin releaseevents using the PPG signals, a stage of digestion may be identified ora level of fasting or hunger may be identified.

Calibration During Ingestion Periods

During ingestion, a greater frequency of insulin release pulses mayaffect the PPG signals. The walls of the blood vessels are constrictingand harden due to muscle tension and may generate false readings ofarterial stiffness or blood flow. The calibration for determiningglucose levels may need to be adjusted during such ingestion periods.

In addition, during insulin release, vascular imaging or tests, such asa CT Scan or ultrasound or MRI of the blood flow of the vascular systemshould be avoided. The walls of the vessels may not exhibit normalbehavior during insulin release. By measuring the frequency or timebetween insulin release events using the PPG signals, a stage ofdigestion may be identified. Depending on the stage of digestion andfrequency of insulin release events, the vascular imaging or tests maybe performed or delayed.

FIG. 37 illustrates a schematic flow diagram of an embodiment of amethod 3700 for identifying a PPG feature, such as an insulin releaseevent. A pulse or amplitude peak is detected in PPG signals at one ormore wavelengths at 3702. The frequency, amplitude and period of the PPGfeature are compared to typical or average responses or characteristicsof PPG signals during an insulin release event at 3704. For example, PPGpulses due to an insulin release have a much lower frequency than aheart rate. The frequency increases after ingestion and then decreaseswith hunger. The period of the pulse for an insulin pulse is longer thana typical heartbeat, for example lasting over 4-10 seconds and have anI_(AC) amplitude that is at least 50% less than a heartbeat pulse. Thus,the biosensor 100 may determine a change in amplitude of the PPG signalsand compare the change in the amplitude of the PPG signals to apredetermined range of the amplitude of PPG signals during an insulinrelease event. The predetermined range may include an average or mean ofthe amplitude or a percentage of change during the insulin releaseevent. The predetermined range of the amplitude may be obtained fromtesting of a general population with a healthy vascular system.

Additionally, the biosensor 100 may determine a period of the pulse andcompare to a predetermined range of periods of PPG pulses during aninsulin release event. The predetermined range may include an average ormean of the period of the pulse during an insulin release event. Thepredetermined range of the period may be obtained from testing of ageneral population with a healthy vascular system.

Furthermore, a frequency or time between pulses may also be determinedand compared to predetermined frequencies or a count of a number ofpulses typically found during digestion or hunger. This comparison maybe used to determine a stage of digestion or level of hunger orestimated time since ingestion of caloric intake.

The frequency, amplitude and period of the PPG feature may also becompared to typical responses of PPG signals during other events, suchas deep inhalation, sepsis or other types of features. Thus, other typesof PPG responses may also be identified.

In addition, one or more parameters derived from the PPG signals may becompared to known patterns or characteristics to identify an insulinrelease pulse at 3706. For example, the I_(AC) signal, an L value curveor an R value curve (such as 390 nm/940 nm) is determined from the PPGsignals. These parameters are then compared to predetermined ranges forthe corresponding parameter during an insulin release event. Forexample, an R value for an insulin pulse is much lower than R values ina sepsis patient. In addition, the R value has a similar pulse shape andtiming as the PPG signal of I_(AC) for an insulin release event whilethere is less correlation between the R value and the I_(AC) signal withdeep inhalation. Other parameters such as integrals or derivatives orwavelet transforms or correlations between PPG signals may be determinedand compared to predetermined normal ranges during insulin releaseevents. The PPG feature is then identified at 3708 as an insulin releaseevent or may be identified as a sepsis condition, deep inhalation orother feature.

When the PPG feature is identified as an insulin release event, thefrequency or time between insulin release events may be measured usingthe PPG signals to determine a stage of digestion or a level of hungerat 3710. A time since ingestion of caloric intake may also be estimated.

Biosensor Configurations

The largest blood vessels are arteries and veins, which have a thick,tough wall of connective tissue and many layers of smooth muscle cells.The wall is lined by an exceedingly thin single sheet of endothelialcells, the endothelium, separated from the surrounding outer layers by abasal lamina. The inner layer (tunica intima) is the thinnest layer,formed from a single continuous layer of endothelial cells and supportedby a subendothelial layer of connective tissue and supportive cells.

Farther from the heart, where the surge of blood has dampened, thepercentage of elastic fibers in an artery's tunica intima decreases andthe amount of smooth muscle in its tunica media increases. The artery atthis point is described as a muscular artery. The diameter of musculararteries typically ranges from 0.1 mm to 10 mm. Their thick tunica mediaallows muscular arteries to play a leading role in vasoconstriction. Incontrast, their decreased quantity of elastic fibers limits theirability to expand.

The radial artery and the proper digital artery to the index finger aremuscular arteries with greater smoother muscle cells. Their thick tunicamedia allows these muscular arteries to play a leading role invasoconstriction. In contrast, their decreased quantity of elasticfibers limits their ability to expand. The radial artery extends toarterioles and capillaries in the fingertip of the index finger. Anarteriole is a small-diameter blood vessel in the microcirculation thatextends and branches out from an artery and leads to capillaries. Anarteriole is a very small artery that leads to a capillary. Arterioleshave the same three tunics as the larger vessels, but the thickness ofeach is greatly diminished. The critical endothelial lining of thetunica intima is intact. The tunica media is restricted to one or twosmooth muscle cell layers in thickness. The tunica externa remains butis very thin. The precise diameter of the lumen of an arteriole at anygiven moment is determined by neural and chemical controls, andvasoconstriction and vasodilation in the arterioles are the primarymechanisms for distribution of blood flow.

Capillaries consist only of the thin endothelial layer of cells with anassociated thin layer of connective tissue. The amounts of connectivetissue and smooth muscle in the vessel wall vary according to thevessel's diameter and function, but the endothelial lining is alwayspresent. In the finest branches of the vascular tree—the capillaries andsinusoids—the walls consist of nothing but endothelial cells and a basallamina, together with a few scattered—but functionallyimportant—pericytes. These are cells of the connective-tissue family,related to vascular smooth muscle cells, that wrap themselves round thesmall vessels. Capillaries consist of a single layer of endothelium andassociated connective tissue without smooth muscle cells.

Due to the different vascular structure at different depths, the use ofan R value of 395 nm/530 nm wavelengths may be preferred in obtainingresults from tissues in a finger or other tissues wherein vessels arecloser to the surface. For example, in some instances it may bepreferred that wavelengths penetrate the tissue at similar depths due tovariations in the vascular profile at different depths. The R valuedescribed herein may also be computed using wavelengths with a lowabsorption coefficient for NO at 440 nm, 530 nm or another wavelength inthe visible range or in the IR range) and a wavelength with a highabsorption coefficient for NO (e.g., at 395 nm or in a range of +/−10 nmof 395 nm). The use of an R value of 395 nm/530 nm wavelengths may bepreferred in obtaining results from tissues in a finger or other tissueswherein vessels are closer to the surface. For example, in someinstances it may be preferred that wavelengths penetrate the tissue atsimilar depths due to variations in the vascular profile at differentdepths.

In addition, due to the different vascular structure at different tissuesites, the biosensor 100 is preferably calibrated for the type of tissueat a detection site. The same detection site is preferably maintainedthroughout a measurement period because vascular structure and dynamicsvaries between different tissue sites. The variation may affect thecalibration and relative amplitude of the PPG signals.

FIG. 38 illustrates an elevational view of a biosensor 3800 configuredfor attachment to a fingertip or toe. The detection site of a fingertipor toe tip is positioned within the two pads. The two pads help preventdisturbance from ambient light. The biosensor 3800 projects the light atthe plurality of wavelengths onto the tissue of the fingertip or toe tipto perform the health measurements.

FIG. 39 illustrates an elevational view of a biosensor 3900 configuredin a ring. The ring may be positioned around a finger or toe. Thebiosensor 3900 projects the light at the plurality of wavelengths ontothe tissue of the finger or toe under the ring to perform the healthmeasurements.

In one or more aspects herein, a processing module or circuit includesat least one processing device, such as a microprocessor,micro-controller, digital signal processor, microcomputer, centralprocessing unit, field programmable gate array, programmable logicdevice, state machine, logic circuitry, analog circuitry, digitalcircuitry, and/or any device that manipulates signals (analog and/ordigital) based on hard coding of the circuitry and/or operationalinstructions. A memory is a non-transitory memory device and may be aninternal memory or an external memory, and the memory may be a singlememory device or a plurality of memory devices. The memory may be aread-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any non-transitory memory device that stores digital information.

As may be used herein, the term “operable to” or “configurable to”indicates that an element includes one or more of circuits,instructions, modules, data, input(s), output(s), etc., to perform oneor more of the described or necessary corresponding functions and mayfurther include inferred coupling to one or more other items to performthe described or necessary corresponding functions. As may also be usedherein, the term(s) “coupled”, “coupled to”, “connected to” and/or“connecting” or “interconnecting” includes direct connection or linkbetween nodes/devices and/or indirect connection between nodes/devicesvia an intervening item (e.g., an item includes, but is not limited to,a component, an element, a circuit, a module, a node, device, networkelement, etc.). As may further be used herein, inferred connections(i.e., where one element is connected to another element by inference)includes direct and indirect connection between two items in the samemanner as “connected to”.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, frequencies, wavelengths, component values,integrated circuit process variations, temperature variations, rise andfall times, and/or thermal noise. Such relativity between items rangesfrom a difference of a few percent to magnitude differences.

Note that the aspects of the present disclosure may be described hereinas a process that is depicted as a schematic, a flowchart, a flowdiagram, a structure diagram, or a block diagram. Although a flowchartmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process is terminatedwhen its operations are completed. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

The various features of the disclosure described herein can beimplemented in different systems and devices without departing from thedisclosure. It should be noted that the foregoing aspects of thedisclosure are merely examples and are not to be construed as limitingthe disclosure. The description of the aspects of the present disclosureis intended to be illustrative, and not to limit the scope of theclaims. As such, the present teachings can be readily applied to othertypes of apparatuses and many alternatives, modifications, andvariations will be apparent to those skilled in the art.

In the foregoing specification, certain representative aspects of theinvention have been described with reference to specific examples.Various modifications and changes may be made, however, withoutdeparting from the scope of the present invention as set forth in theclaims. The specification and figures are illustrative, rather thanrestrictive, and modifications are intended to be included within thescope of the present invention. Accordingly, the scope of the inventionshould be determined by the claims and their legal equivalents ratherthan by merely the examples described. For example, the componentsand/or elements recited in any apparatus claims may be assembled orotherwise operationally configured in a variety of permutations and areaccordingly not limited to the specific configuration recited in theclaims.

Furthermore, certain benefits, other advantages and solutions toproblems have been described above with regard to particularembodiments; however, any benefit, advantage, solution to a problem, orany element that may cause any particular benefit, advantage, orsolution to occur or to become more pronounced are not to be construedas critical, required, or essential features or components of any or allthe claims.

As used herein, the terms “comprise,” “comprises,” “comprising,”“having,” “including,” “includes” or any variation thereof, are intendedto reference a nonexclusive inclusion, such that a process, method,article, composition or apparatus that comprises a list of elements doesnot include only those elements recited, but may also include otherelements not expressly listed or inherent to such process, method,article, composition, or apparatus. Other combinations and/ormodifications of the above-described structures, arrangements,applications, proportions, elements, materials, or components used inthe practice of the present invention, in addition to those notspecifically recited, may be varied or otherwise particularly adapted tospecific environments, manufacturing specifications, design parameters,or other operating requirements without departing from the generalprinciples of the same.

Moreover, reference to an element in the singular is not intended tomean “one and only one” unless specifically so stated, but rather “oneor more.” Unless specifically stated otherwise, the term “some” refersto one or more. All structural and functional equivalents to theelements of the various aspects described throughout this disclosurethat are known or later come to be known to those of ordinary skill inthe art are expressly incorporated herein by reference and are intendedto be encompassed by the claims. Moreover, nothing disclosed herein isintended to be dedicated to the public regardless of whether suchdisclosure is explicitly recited in the claims. No claim element isintended to be construed under the provisions of 35 U.S.C. § 112(f) as a“means-plus-function” type element, unless the element is expresslyrecited using the phrase “means for” or, in the case of a method claim,the element is recited using the phrase “step for.”

1. A device, comprising: an optical circuit configured to detectphotoplethysmography (PPG) signals, wherein a first PPG signal includesa first spectral response around a first wavelength obtained from lightreflected from or transmitted through tissue of a user and a second PPGsignal includes a second spectral response around a second wavelengthobtained from light reflected from or transmitted through the tissue ofthe user; and one or more processing circuits configured to: identify aninsulin release event using the first PPG signal and the second PPGsignal, wherein the insulin release event is a pulse of insulin in bloodflow of the user; and determine a frequency of insulin release events.2. The device of claim 1, wherein the one or more processing circuitsare further configured to determine to perform tests or vascular imagingin response to the frequency of insulin release events.
 3. The device ofclaim 1, wherein the one or more processing circuits are furtherconfigured to determine to delay vascular imaging or tests in responseto the frequency of insulin release events.
 4. The device of claim 1,wherein the one or more processing circuits are further configured toidentify the insulin release event by: determining an R value curveusing a ratio value obtained from a first AC component of the first PPGsignal and a second AC component of the second PPG signal; andidentifying the insulin release event using the R value curve.
 5. Thedevice of claim 4, wherein the one or more processing circuits arefurther configured to identify the insulin release event by: comparingthe R value curve to one or more R value curve patterns indicative of aninsulin release event.
 6. The device of claim 1, wherein the one or moreprocessing circuits are further configured to determine an insulin levelduring the insulin release event by: determining an R value curve duringthe insulin release event using a ratio value obtained from a first ACcomponent of the first PPG signal and a second AC component of thesecond PPG signal; determining an integral area of the R value curveduring the insulin release event; and determining the insulin levelusing the area of the R value curve and a calibration.
 7. The device ofclaim 1, wherein the one or more processing circuits are furtherconfigured to: identify a number of insulin release events during apredetermined time period; and determine at least one of: a stage ofdigestion, an estimated time since caloric intake or a level of hunger.8. The device of claim 1, wherein the one or more processing circuitsare further configured to: determine a correlation signal during theinsulin release event between the first PPG signal and the second PPGsignal, wherein the correlation signal includes a phase delay betweenthe first PPG signal and the second PPG signal or a pulse shapecorrelation between the first PPG signal and the second PPG signal. 9.The device of claim 8, wherein the one or more processing circuits arefurther configured to determine a level of vasoconstriction orvasodilation using the correlation signal during the insulin releaseevent.
 10. The device of claim 9, wherein the one or more processingcircuits are further configured to: compare the level ofvasoconstriction or vasodilation to a predetermined range measured froma general population with healthy vascular systems; and determine abalance of efficacy of endothelin (ET-1) and nitric oxide (NO) duringthe insulin release event.
 11. The device of claim 8, wherein the one ormore processing circuits are further configured to determine ameasurement of vascular health using the correlation signal.
 12. Thedevice of claim 1, wherein the one or more processing circuits arefurther configured to: determine a vascular dysfunction in the user;determine a ratio value obtained from a first AC component of the firstPPG signal and a second AC component of the second PPG signal; access anindividual calibration table between predetermined ratio values andglucose levels; and obtain a glucose level using the individualcalibration and the ratio value.
 13. A system, comprising: an opticalcircuit configured to: obtain at least a first PPG signal including afirst spectral response around a first wavelength obtained from lightreflected from or transmitted through tissue of a user; at least oneprocessing circuit configured to: detect an insulin release event usingthe first PPG signal, wherein the insulin release event is a pulse ofinsulin in blood flow of the user; and determine to delay vascularimaging or tests based on detection of the insulin release event. 14.The system of claim 13, wherein the at least one processing circuit isfurther configured to determine a frequency of insulin release eventsduring a measurement period.
 15. The system of claim 14, wherein the atleast one processing circuit is configured to determine to delayvascular imaging or tests based on the frequency of insulin releaseevents during a measurement period.
 16. The system of claim 13, whereinthe at least one processing circuit is configured to determine toperform vascular imaging or tests when no insulin release events aredetected during a measurement period.
 17. The system of claim 13,wherein the first wavelength is in a range from 380 nm to 410 nm.
 18. Abiosensor, comprising: an optical circuit configured to: obtain a firstPPG signal including a first spectral response around a first wavelengthobtained from light reflected from or transmitted through tissue of auser; at least one processing circuit configured to: determine afrequency of insulin release events using the first PPG signal, whereinthe insulin release events are a pulse of insulin in blood flow of theuser.
 19. The biosensor of claim 18, wherein the at least one processingcircuit is further configured to determine a frequency of insulinrelease events using the first PPG signal by: identifying a number ofinsulin release events during a measurement period.
 20. The biosensor ofclaim 18, wherein the at least one processing circuit is furtherconfigured to determine at least one of: a stage of digestion, anestimated time since caloric intake, or a level of hunger.